{"as_of":"2026-08-14T01:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eb04f0c4364709f861361ad76a3def8a16397ac286a5154ce213141a86d17363","coverage":[{"denominator":81,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":81,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:14:20.262245Z","state":"measured"},{"denominator":81,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":81,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.07080/citation-record","integrity":"/paper/2412.07080/integrity","json":"/paper/2412.07080/citation-record.json","paper":"/paper/2412.07080"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:19.923998Z","title":"Mead c.(1991). the silicon retina,","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.923998Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:8f77ad698b6a8a0a690b7d8aeaa9bd94355a0a040b783e657f35b2d6707cadab","observation_id":"0853bcb5-e06f-4649-b752-c2a3950859ae","resolution":{"observed_at":"2026-08-11T19:14:19.923998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.151214Z","title":"A 128 ×128 120 db 15 µs latency asynchronous temporal contrast vision sensor,","venue":null,"work_id":"c2fea07e-75b3-482a-88ff-3dcd15f8baaa","year":2008},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.928491Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:98926f935a165ed459ca1dcaf77496531c5b38c420c41713b82f84830922d092","observation_id":"e7d696e8-9935-489c-bb10-8633470680a2","resolution":{"observed_at":"2026-08-11T19:14:21.155400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.139033Z","title":"A 240× 180 10mw 12us latency sparse-output vision sensor for mobile applications,","venue":null,"work_id":"aac583ec-4619-4866-b8f7-592a0067a4db","year":2013},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.932698Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:b871c8664e36421d39013dc357484b800ccfd25d602d601626416fe71623a5b1","observation_id":"96062763-1195-4220-a5b9-a97099b64866","resolution":{"observed_at":"2026-08-11T19:14:21.143266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.127527Z","title":"A qvga 143 db dynamic range frame-free pwm image sensor with lossless pixel-level video com- pression and time-domain cds,","venue":null,"work_id":"d20bc298-0130-45af-bde2-2338ddfd7575","year":2010},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.936856Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:e4f88483eda42bc5fa1a04b11a918180ccdad39d46614a791265c1f83a0d034b","observation_id":"be130f96-94a1-42c1-911a-61e69236abfd","resolution":{"observed_at":"2026-08-11T19:14:21.131304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.115825Z","title":"Fast sensory motor control based on event-based hybrid neuromorphic-procedural system,","venue":null,"work_id":"2d3df46c-a8ce-40fe-ae8e-7e356e41ea9a","year":2007},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.940657Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:e172799f60de41c9fd963a99f5e12f9f306829155b8783d774c7adb80a4219eb","observation_id":"95255641-16bd-435d-9ec0-90221a5bb3f7","resolution":{"observed_at":"2026-08-11T19:14:21.119952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.103403Z","title":"Event-based, 6-dof camera tracking from photometric depth maps,","venue":null,"work_id":"e9cc71ab-3203-4057-9cab-0579c3e838e6","year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.944613Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:bc55e3c1f4633b68d7f79e6f34b5f33a45d6faee68df324e7c6f5a7ee0c5e93e","observation_id":"b002e288-391c-421b-ba93-3cb6a00d5115","resolution":{"observed_at":"2026-08-11T19:14:21.107966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:19.948763Z","title":"High speed and high dynamic range video with an event camera,","venue":null,"work_id":null,"year":1964},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.948763Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:80d430585a2c968f93ef3cef5f0707a3511a7d6e3486a8c2327c14b67ebefd38","observation_id":"88f3631c-fcf3-4275-94c2-3f9961da332a","resolution":{"observed_at":"2026-08-11T19:14:19.948763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:19.952746Z","title":"Event-based semantic segmentation with posterior attention,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.952746Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:d291c0bc20b1cd5700d6c1dc3c57265fe40583e6892fdd3d8e2b6c37a6c989fc","observation_id":"abce5836-bf92-45c2-ac8c-ff8d87d9b79d","resolution":{"observed_at":"2026-08-11T19:14:19.952746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.078874Z","title":"Asynchronous event-based fourier analysis,","venue":null,"work_id":"f24b98ed-d76c-4b42-9c9d-d2ee4b44d011","year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.956379Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:b597ecaba59c439e3059a336b5d87c451fb0a7809f95082db5bee5a17e9d0754","observation_id":"37f605be-118d-4b88-bfd0-ba9aab6e1d92","resolution":{"observed_at":"2026-08-11T19:14:21.082607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.067926Z","title":"Spade-e2vid: Spatially-adaptive denormalization for event-based video reconstruc- tion,","venue":null,"work_id":"1659a58f-5395-4de5-9a5e-ac0d58d6e63c","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.960159Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:7c304850fb75f0257bc510e03abd827d00bfe33d560e12594091cc331fa32cee","observation_id":"aa6eefd1-a343-4b1b-a0b5-f5b8c49aabe0","resolution":{"observed_at":"2026-08-11T19:14:21.071713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:19.963939Z","title":"Low-latency visual odometry using event-based feature tracks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.963939Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:121e69a9c6f4397b9950bc28f98287bf0c049d4d9f231a2f34473f835ce5cb22","observation_id":"9f717bde-b78b-4ed4-ab70-2965dec1cdcd","resolution":{"observed_at":"2026-08-11T19:14:19.963939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.048267Z","title":"Real-time 3d reconstruction and 6-dof tracking with an event camera,","venue":null,"work_id":"6a377577-11e3-4f2e-b83c-942d2b47e320","year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.967811Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:592abf83a6a9f8a9116dd52fe9adc8511a9c300f831ab93c990a179ce2388f43","observation_id":"cf841dd2-4a18-4795-831c-0450cc6103f0","resolution":{"observed_at":"2026-08-11T19:14:21.052225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.036127Z","title":"The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and slam,","venue":null,"work_id":"a4037629-9044-4cfc-aadf-d00a58e99470","year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.971330Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:67b3a623de071100e1bada7a6e243ad8cd865d8dc7ea12e6d7352b8eb9b38717","observation_id":"10d33c30-8a8f-4ce2-b3fc-a7e502aceea3","resolution":{"observed_at":"2026-08-11T19:14:21.039995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.023424Z","title":"Hfirst: A temporal approach to object recognition,","venue":null,"work_id":"2ae56d17-e674-4611-be42-bf1ddf1b1fce","year":2015},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.974781Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:afbb308e540b6f5ea10a79cfaf45d2375e82b7d60b1203dc4b8e64b1ad4292f3","observation_id":"4f78439b-6543-4cf7-9bcb-6aeacc9a8f08","resolution":{"observed_at":"2026-08-11T19:14:21.027677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:21.010354Z","title":"Hots: a hierarchy of event-based time-surfaces for pattern recognition,","venue":null,"work_id":"98d8acfa-e423-45ae-8b35-5af6cc66c4ad","year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.978495Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:276f22444693ea05420fabeb608bece5cb1cb9a76b2fa3b2e5010d8d3ada4fe5","observation_id":"82c2668b-9f8b-4409-9936-cfab8383d668","resolution":{"observed_at":"2026-08-11T19:14:21.014621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.997767Z","title":"Hats: Histograms of averaged time surfaces for robust event-based ob- ject classification,","venue":null,"work_id":"31322d79-53ea-4ad1-a4b4-95471eb4ae83","year":2018},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.982057Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:7cb1fc3cc13b105aecd29e4389bcaf717e86a68ce7a36fd96f300154b3cca130","observation_id":"8dc55897-ea60-4f33-bc7e-36e45bb2c590","resolution":{"observed_at":"2026-08-11T19:14:21.002375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:19.985951Z","title":"Graph-based object classification for neuromorphic vision sensing,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.985951Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:7e74e5912e44d8b48c22f5ef96af0fe3a89669407d8049d6fe2c2cc96beb7c08","observation_id":"653a5519-e42b-4759-a64e-c8785172eff7","resolution":{"observed_at":"2026-08-11T19:14:19.985951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.978904Z","title":"Spike-based motion esti- mation for object tracking through bio-inspired unsupervised learning,","venue":null,"work_id":"d16c4f7a-b979-4b30-ac9b-75b561582c1d","year":2022},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.989808Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:2fdc5f92ee435802b5a05024acaa417fa4162d1d40a754b4e6ef7c53d6c778dc","observation_id":"3d148bd0-743c-4739-85c0-8491aa21a449","resolution":{"observed_at":"2026-08-11T19:14:20.983085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.06898","last_updated":"2018-08-13T15:32:01Z","snapshot_observed_at":"2026-07-06T06:24:15.593553Z","submitted_at":"2018-02-19T22:47:52Z","title":"EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.06898","snapshot_observed_at":"2026-08-11T19:14:19.993976Z","title":"Ev-flownet: Self- supervised optical flow estimation for event-based cameras,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.993976Z"},"links":{"cited_paper":"/paper/1802.06898","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:0f682cbb37aadd0064cda7bbdf2c68e52b085c456d8cdf567a71da32e90ccb14","observation_id":"ad4dbb26-e2be-4e8a-af87-fcb5598960b8","resolution":{"observed_at":"2026-08-11T19:14:19.993976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.966770Z","title":"Unsupervised event-based learning of optical flow, depth, and egomotion,","venue":null,"work_id":"f12ea3f9-16cc-46f1-aaaa-3a3c8d5dbb0b","year":2019},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:19.998696Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:e77de631dbd53ab786a72e0b6708b1f3681b1a5ddec203784faf3345bbb6d52e","observation_id":"a2903fbb-5f7c-4ef3-9a98-1a3b0d37b569","resolution":{"observed_at":"2026-08-11T19:14:20.970955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.954151Z","title":"End- to-end learning of representations for asynchronous event-based data,","venue":null,"work_id":"c600cf5e-7407-4461-b9d6-51e5a53736f4","year":2019},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.003130Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:99e2a596d56ef36a5f2f805915b0941cebc52e6bfd9226d7442c5b5e7c468c1c","observation_id":"e767f191-93d5-4742-a745-9b1c9a528faf","resolution":{"observed_at":"2026-08-11T19:14:20.958793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.943184Z","title":"A differen- tiable recurrent surface for asynchronous event-based data,","venue":null,"work_id":"664ab761-87f4-421b-be05-35878b9d5a95","year":2020},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.008013Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:5ee26777586323a83f4065a3e1ec3b683fd097fe8505ca9a8a37dd7546949132","observation_id":"e686ae6e-885d-47d4-a128-fb56cd177528","resolution":{"observed_at":"2026-08-11T19:14:20.947043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.012802Z","title":"Training deep spiking neural networks using backpropagation,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.012802Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:c24b6008c2a7f4fc0c8f9de3faadd77d3770437a24a0263d6972b23d1e4e2572","observation_id":"23c66175-ee12-408f-961f-49da70f99453","resolution":{"observed_at":"2026-08-11T19:14:20.012802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.924694Z","title":"Feed- forward categorization on aer motion events using cortex-like features in a spiking neural network,","venue":null,"work_id":"df84018a-50f5-4175-996b-56f85e9859a1","year":1963},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.017152Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:024085cdb2913745b312aa1104cbe26b9678b77898f2c2a04e8683f183c2687f","observation_id":"6fdad58a-95e4-4126-b160-5425a4f28528","resolution":{"observed_at":"2026-08-11T19:14:20.928453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.912769Z","title":"Event-based visual flow,","venue":null,"work_id":"3e6245d1-0408-4934-9e6f-b90f0342c705","year":2013},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.024271Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:b901823160c05660a4669c0241be16752a38be826a55c18edafd3a4dddf64e78","observation_id":"0e6d1110-d9a9-44a1-84cb-ed5418ddf401","resolution":{"observed_at":"2026-08-11T19:14:20.916956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.029427Z","title":"Event-based vision meets deep learning on steering prediction for self- driving cars,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.029427Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:c263d6265b204335b141c61492b5f20201c7f698b3d7231a43fad5b47b89238d","observation_id":"57e1df9b-1a1f-41ea-81db-858c94cbab71","resolution":{"observed_at":"2026-08-11T19:14:20.029427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.894385Z","title":"Event probability mask (epm) and event denoising convolutional neural network (edncnn) for neuromorphic cameras,","venue":null,"work_id":"76510dd2-72be-4c30-98a8-276225e8fe35","year":2020},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.033659Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:57c4f451b96e272a1efca0d610f882d059d990467a7dc613267007de78bbf129","observation_id":"cb544550-e384-49a3-bf91-d8df36ccc2cd","resolution":{"observed_at":"2026-08-11T19:14:20.898464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.881775Z","title":"Low cost and latency event camera background activity denoising,","venue":null,"work_id":"3d093c0c-092e-4642-9d18-6eba9442c56b","year":2022},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.038494Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:145eb34657d81696ae1306ad4ddcd84ef1b7b5cfbb03c58622be4c0337b43fb4","observation_id":"2242a19a-663e-4c7c-8ef9-e753bfc39306","resolution":{"observed_at":"2026-08-11T19:14:20.886413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.869738Z","title":"Probabilistic undirected graph based denoising method for dynamic vision sensor,","venue":null,"work_id":"bb472bce-6f4d-407f-abd1-a3c233bcfbd5","year":2020},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.042612Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:21fe9a1ee7927efeea55ba3628c8d7692ac4f2271568360eabad78ac29b63eee","observation_id":"ab4dee0e-8966-4e72-9a79-fd843ff34d38","resolution":{"observed_at":"2026-08-11T19:14:20.874418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.855609Z","title":"Mapping from frame- driven to frame-free event-driven vision systems by low-rate rate coding and coincidence processing–application to feedforward convnets,","venue":null,"work_id":"2a7a2a87-3e7a-4c4d-a158-22016a20d27a","year":2013},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.047406Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:c2e81a20537585ea6db9cab2eb5dc15221c660644d91267ed29c0a181cdfceb3","observation_id":"e50940de-93c7-4c04-8474-0e55204b38d8","resolution":{"observed_at":"2026-08-11T19:14:20.859941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.843149Z","title":"Spiking neural networks for frame-based and event-based single object localization,","venue":null,"work_id":"09b22af2-0ba4-4ad3-966c-b41294d0bdb9","year":2023},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.052658Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:05348b62c3d99c6fe5b6597e1b86d4f63f1ee739eef2015831e15f4822293b84","observation_id":"2f8eb787-bb70-4673-b33f-885a5916e43f","resolution":{"observed_at":"2026-08-11T19:14:20.847015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.832087Z","title":"Asynchronous frameless event-based optical flow,","venue":null,"work_id":"3a83d067-fa67-441a-bbad-21080ea52720","year":2012},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.056770Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:6800589a23bc7765a6af3e85b599a47c775b8c3fc7f6ae81c7c61cb243a47b2b","observation_id":"25790507-6e44-46dd-bbf9-49ce16fd7b98","resolution":{"observed_at":"2026-08-11T19:14:20.835830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.820092Z","title":"Phased lstm: Accelerating recurrent network training for long or event-based sequences,","venue":null,"work_id":"1dc6ab32-04c7-44d1-8828-9178f9f30b70","year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.061027Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:6a5ea25389c1ea24636ef753f48dd68e0e1e9ae658ecaa673107794b6fcea171","observation_id":"b9109861-d914-4d16-b127-9fff46cf66a4","resolution":{"observed_at":"2026-08-11T19:14:20.825173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.808731Z","title":"Learning from images: A distillation learning framework for event cameras,","venue":null,"work_id":"3c947433-e872-4ba5-8e1a-535d8dc3f60f","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.065299Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:673e3ae4383636bf51db2240011f94ffba3ad3bc9eb88059917c2fd566beeaf7","observation_id":"a6b07851-9854-4373-955c-dfd7f1fce4d0","resolution":{"observed_at":"2026-08-11T19:14:20.812493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.795966Z","title":"Spatio-temporal recurrent networks for event-based optical flow estima- tion,","venue":null,"work_id":"2a148db0-0d86-4341-aa30-d39477f01a37","year":2022},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.069458Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:226bacc24b950664e1491d6a8f958160412e28d7d1228de6bd726e4468f3d893","observation_id":"71587400-65a4-4582-9b3f-799dd2bcc3a5","resolution":{"observed_at":"2026-08-11T19:14:20.800610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.073872Z","title":"Graph-based spatio-temporal feature learning for neuromorphic vision sensing,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.073872Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:56f07c4100c08ae1bc29034b73c31910a4ecb3e1b1267aa6b2a5d105c8efe4ee","observation_id":"4684da43-97b0-4254-8157-b981ed24457d","resolution":{"observed_at":"2026-08-11T19:14:20.073872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.777009Z","title":"Bina-rep event frames: A simple and effective representation for event-based cameras,","venue":null,"work_id":"c09dfb05-33e9-4433-88a9-bf9f8ee27d68","year":2022},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.078843Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:ec3654285d469577c3f1fbba0139d9533551b51871ee97a8e80df961cc140f9d","observation_id":"9cd0f914-9a73-40d2-8962-c7f933ebff7a","resolution":{"observed_at":"2026-08-11T19:14:20.781154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.764439Z","title":"Exploring joint embedding architectures and data augmentations for self-supervised representation learning in event-based vision,","venue":null,"work_id":"fda223e2-1047-43c6-b0f5-3a7f7cce2398","year":2023},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.082751Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:9ac6a2e6246bb57246ab2e9d8bf740fb65dcb7c383920a42c62f5dfde58d2dc5","observation_id":"d7a4452a-fa03-480d-8b5d-c98c63d8472c","resolution":{"observed_at":"2026-08-11T19:14:20.768894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.750316Z","title":"Ev- gait: Event-based robust gait recognition using dynamic vision sensors,","venue":null,"work_id":"86a76640-b084-4ea8-a6b7-77088b87485b","year":2019},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.086591Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:becbe374c1e6a3f51ea54476fd918accc530862200cef757ee7934d6bb0fd9e0","observation_id":"78980887-a163-43e2-a52f-b7f03b144952","resolution":{"observed_at":"2026-08-11T19:14:20.754254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.737224Z","title":"Event-stream representation for human gaits identification using deep neural networks,","venue":null,"work_id":"c51159b5-3106-4890-bda5-6ea704eb1d9c","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.091250Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:3c918ec44e5f51480d9b18497b0d0a2509f50713445794d0c1dd5d7cce5a0eda","observation_id":"075d24ab-a532-4473-bbee-2499277a915d","resolution":{"observed_at":"2026-08-11T19:14:20.741695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.725713Z","title":"Event-based video reconstruction using transformer,","venue":null,"work_id":"bf34236e-fce6-441c-b8fc-0de7557aa657","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.095754Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:a7315bd54c8ac170ee7c77282be307dec20e50b2ccea47bb7d58c42def278c2f","observation_id":"553662c2-1ea1-495c-93b2-a7e43f279098","resolution":{"observed_at":"2026-08-11T19:14:20.729726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.714859Z","title":"Back to event basics: Self- supervised learning of image reconstruction for event cameras via photometric constancy,","venue":null,"work_id":"93b2cdce-78b5-44a7-8c75-75b69d5332c4","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.099896Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:8e4d8ea9200f80f6e6421270457691f5b009c019d23982f156004f94acbc23ca","observation_id":"7f3ab803-c095-4a55-a4b3-3924da0a802c","resolution":{"observed_at":"2026-08-11T19:14:20.718633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.703301Z","title":"Accurate and efficient frame-based event representation for aer object recognition,","venue":null,"work_id":"b7ec84ac-474f-4309-96e3-9501b74ff5d6","year":2022},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.103830Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:23ce455737a6f2814f9c152a7cbfe0569f4b270fb351af7cd1bc81b5ccb3a0e5","observation_id":"45e32482-3c38-4486-b183-49df49e43997","resolution":{"observed_at":"2026-08-11T19:14:20.707835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.107685Z","title":"Time-ordered recent event (tore) volumes for event cameras,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.107685Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:3d1e832e6cb762a300cc6944d4a155811477eceaea456c5834cbbab73c9ebb2d","observation_id":"5a4fb5a1-e3f7-4d9b-ab8e-db41049f6f7f","resolution":{"observed_at":"2026-08-11T19:14:20.107685Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.684943Z","title":"Comparing representations in tracking for event camera-based slam,","venue":null,"work_id":"9ec61c11-be5d-4813-a5f4-4bb61322c2d9","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.111697Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:b138c3656b1a842046fd3f26eee807ab77e914e223c951fc97f3d38a5673dcad","observation_id":"54c3b5c3-5c6b-4cc1-a25d-98214341fabe","resolution":{"observed_at":"2026-08-11T19:14:20.688738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.115525Z","title":"Motion robust high-speed light-weighted object detection with event camera,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.115525Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:d6117b7cc2d1308d160e2955883877302acc140a037d6677df5ae76c8a0f84ca","observation_id":"3c13327f-9fb3-4524-94a4-019d418d10ef","resolution":{"observed_at":"2026-08-11T19:14:20.115525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.667233Z","title":"Adaptive global decay process for event cameras,","venue":null,"work_id":"85cfeb6a-b96f-45f6-9366-4ffb26516a11","year":2023},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.119296Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:0545f4b0751f5c3f4bcc208319b7810010d64b0543a3051376ce41cad2a7ef0d","observation_id":"10c4b896-3159-4459-8bec-ee3f5582c1ac","resolution":{"observed_at":"2026-08-11T19:14:20.671039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-12T14:19:29.389332Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-11T19:14:20.123796Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.123796Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:cc75fa97717ca8c4cc40fe688e925389230b13a5a09935e5d342e13dc01a6b29","observation_id":"8c3b484a-ce1f-45e3-bce5-6a310978c32d","resolution":{"observed_at":"2026-08-11T19:14:20.123796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.656365Z","title":"Inception-v4, inception-resnet and the impact of residual connections on learning,","venue":null,"work_id":"51ebc268-ab0b-4640-be22-d26cd6c88429","year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.127982Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:3cf673ed0915d277364f92e0a37e76908b6c8b3a8c6904b5824faf3780270531","observation_id":"1cd48eb3-3383-4f27-9bb4-546261df0459","resolution":{"observed_at":"2026-08-11T19:14:20.660320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.131959Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.131959Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:0b05027b3d8c5a5834abc041cb22c410807b3249ed3af853a48cef50f7e7897b","observation_id":"111f0213-e948-4192-9254-d7df8af20fd9","resolution":{"observed_at":"2026-08-11T19:14:20.131959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-11T19:14:20.136986Z","title":"Mobilenets: Efficient convo- lutional neural networks for mobile vision applications,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.136986Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:abfd07021511f9cc4c2ba0cd6758157224d0be1abdbc00fd325913de1dc50874","observation_id":"0dbd6d68-8d53-4de9-b4f5-04038b408317","resolution":{"observed_at":"2026-08-11T19:14:20.136986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.142422Z","title":"Mobilenetv2: Inverted residuals and linear bottlenecks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.142422Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:fd15a1d8c6c95e83e51b13d5e8fb11ca71d96bb5a2a30fcee0477cd7bd6e0146","observation_id":"da7e0faf-4bc3-47de-835d-d96b188676b1","resolution":{"observed_at":"2026-08-11T19:14:20.142422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.146567Z","title":"Squeeze-and-excitation networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.146567Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:74eb1efe65e637a54d82ab0b31e925b9c9fd91c4f736a9e1d2e09a7d76627f5d","observation_id":"a55eb499-f1c7-4eb9-aa0a-1832ee1e48ec","resolution":{"observed_at":"2026-08-11T19:14:20.146567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.150442Z","title":"Efficientnet: Rethinking model scaling for con- volutional neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.150442Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:19ba6e709a76060e2f593b95893a3c689ea6f67f7bad0c97688ad3bdb933b1e9","observation_id":"c543cad3-a7c6-471a-ba82-a92a7e0b571e","resolution":{"observed_at":"2026-08-11T19:14:20.150442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.617951Z","title":"Efficientnetv2: Smaller models and faster training,","venue":null,"work_id":"a050fcd8-5844-4ab8-b991-08f4f5a1b22e","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.154622Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:e70e4655e9326ea94b63c6b9b23bd5f14a05f34318d223a05d5dfe8cc7b9dc90","observation_id":"26168874-809b-4d2d-a0e4-a6618f2520ad","resolution":{"observed_at":"2026-08-11T19:14:20.621805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.158428Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.158428Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:c19ad7ceb44e1d432dbb3be4f845fa090dbb24526fb8ec3de1d678c49abada9d","observation_id":"562d350e-c74f-4dbb-bef2-bbc4822f713e","resolution":{"observed_at":"2026-08-11T19:14:20.158428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.599677Z","title":"Event density based denoising method for dynamic vision sensor,","venue":null,"work_id":"a8cf10ea-7514-456b-90f4-4d5f2d6811cf","year":2024},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.162695Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:10116c09b1d4c3805f01680c05d1942e05c702866fa171b01b146969e34ad0ed","observation_id":"a21b8ae3-35ae-4809-8086-de1880a59f0c","resolution":{"observed_at":"2026-08-11T19:14:20.603466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.586814Z","title":"Eventzoom: Learning to denoise and super resolve neuromorphic events,","venue":null,"work_id":"4cbd7f08-de23-4075-ad7a-aad57bc0360b","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.166826Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:3a5adaa45916215534c7d14ef7f01a302960637c645738cd3237076cf85cbe7d","observation_id":"184e4358-98be-4590-9708-72381e7bef49","resolution":{"observed_at":"2026-08-11T19:14:20.591366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.575405Z","title":"Ced: Color event camera dataset,","venue":null,"work_id":"09cf4527-3aa6-48d3-a865-15ed8bd64853","year":2024},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.170613Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:a8f793c6704857c2ce103e69dfb558bcd0f7fb64de55867167985e7c309ae1e0","observation_id":"20969c0f-324e-4186-b003-f1f199286886","resolution":{"observed_at":"2026-08-11T19:14:20.579450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.564221Z","title":"Reducing the sim-to-real gap for event cameras,","venue":null,"work_id":"4adcd842-edcb-4aa1-812b-bc1030e52cb6","year":2020},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.175220Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:e0e163661f0f6d32f5ba2f9079f6af9ddd65306beb95133c288c31742ba8415d","observation_id":"715c3f61-95c1-4098-ae6e-112464d52c54","resolution":{"observed_at":"2026-08-11T19:14:20.568193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01458","last_updated":"2017-11-04T16:19:56Z","snapshot_observed_at":"2026-08-09T13:46:20.881108Z","submitted_at":"2017-11-04T16:19:56Z","title":"DDD17: End-To-End DAVIS Driving Dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01458","snapshot_observed_at":"2026-08-11T19:14:20.179144Z","title":"Ddd17: End-to-end davis driving dataset,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.179144Z"},"links":{"cited_paper":"/paper/1711.01458","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:58e46a701fd72cbc07e6061fcfcce0808936d3da7a8a1afe9e3217be29f94b1c","observation_id":"7ef467df-51d6-4e18-b880-5ebefba3e5a8","resolution":{"observed_at":"2026-08-11T19:14:20.179144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.183857Z","title":"The multivehicle stereo event camera dataset: An event camera dataset for 3d perception,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.183857Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:a414182dc481085e88f73716f464cb7dabcc620fe1542dacb1e19f183584e31a","observation_id":"ef2abf85-f75e-4800-bef4-a11588de3cef","resolution":{"observed_at":"2026-08-11T19:14:20.183857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-12T08:59:05.030983Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-11T19:14:20.188009Z","title":"Layer normalization,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.188009Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:abb38779462235de4e6529004db25e7ed42f5ffec2655a82e7772eb7f01be1ea","observation_id":"ba8934a4-bc07-49c6-a255-7aefe33a1bbe","resolution":{"observed_at":"2026-08-11T19:14:20.188009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.05941","last_updated":"2017-10-27T17:45:21Z","snapshot_observed_at":"2026-08-08T18:23:31.977872Z","submitted_at":"2017-10-16T18:05:45Z","title":"Searching for Activation Functions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.05941","snapshot_observed_at":"2026-08-11T19:14:20.192113Z","title":"Searching for activation functions,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.192113Z"},"links":{"cited_paper":"/paper/1710.05941","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:d2aa1b4e54301321bb4328d7698e36565b83b44fb3df4fae6f05712abe7c1e54","observation_id":"d4ba2a10-eed5-4dc4-8751-21470005b49c","resolution":{"observed_at":"2026-08-11T19:14:20.192113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.197728Z","title":"Event- based vision: A survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.197728Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:d39d7529c01e918e0ea8e0c41e196cd321e80045de7fbd99d56b46cdb04431ec","observation_id":"47103553-24ce-4492-ab55-0bca76b21601","resolution":{"observed_at":"2026-08-11T19:14:20.197728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.538588Z","title":"The regression analysis of binary sequences,","venue":null,"work_id":"f917c8b2-0c8f-4a47-8dee-f212101be8d6","year":1958},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.202610Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:921496a1d941d0b2d37522c341660352dd5220574ad4761fcebbddad9322181a","observation_id":"7a07239b-5221-4cbb-a933-ea3c4eab9605","resolution":{"observed_at":"2026-08-11T19:14:20.542297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.525913Z","title":"A quantitative analysis of current practices in optical flow estimation and the principles behind them,","venue":null,"work_id":"264ca65d-e254-4180-b8d1-7a2815418694","year":2014},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.206757Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:cb9d8abeb03a8ff3f613447cbca39b7c7b0d8791f849588a334e74471608ef3d","observation_id":"ca417b89-f5f0-4485-87ff-daceebdfad34","resolution":{"observed_at":"2026-08-11T19:14:20.530292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.210801Z","title":"Converting static image datasets to spiking neuromorphic datasets using saccades,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.210801Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:f4a5a7d7747236eac0bcd20f95d7c69a51ae4b2f9da055407710cc9028bc8f2f","observation_id":"1a938937-aef3-4e92-ae08-01564cf37135","resolution":{"observed_at":"2026-08-11T19:14:20.210801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.506387Z","title":"Cifar10-dvs: an event-stream dataset for object classification,","venue":null,"work_id":"dedb4596-7d23-421c-8c15-62ccade38544","year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.215102Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:782534f39e786a126ef34270c5d8dd92bb239ca38376139f921297edf601e0ab","observation_id":"a9219f95-d285-49be-bf48-fa6159f8cee8","resolution":{"observed_at":"2026-08-11T19:14:20.510209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-13T11:38:10.906031Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-11T19:14:20.218900Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.218900Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:928b10695eac37280a7f42a39ba287533d5fbb6eb442e1d342581e79e3636fd9","observation_id":"6e5285db-1795-419f-8af1-06f0e7ce5012","resolution":{"observed_at":"2026-08-11T19:14:20.218900Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.223268Z","title":"Convolutional neural networks on graphs with fast localized spectral filtering,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.223268Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:8894db66ed2bf5504d6af5a759f1368e3c940042b870d993ea9788ecce2169f6","observation_id":"8e8011e1-59d2-4291-abbb-763fc4d4d29d","resolution":{"observed_at":"2026-08-11T19:14:20.223268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.227043Z","title":"Geometric deep learning on graphs and manifolds using mixture model cnns,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.227043Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:5d5b2569ff2db88c1201d367ddd78b1751e30caea782bbb5d3cf4451016ec6c3","observation_id":"4f0915ef-2b1a-48e0-8fa3-ee43e211f13b","resolution":{"observed_at":"2026-08-11T19:14:20.227043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-08-13T05:06:48.606308Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-08-11T19:14:20.230862Z","title":"How powerful are graph neural networks?","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.230862Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:1b48c5b2c4f10d171476c9d039be4d4bc25395b55b4d3e2c9844e4901ae41f26","observation_id":"46371f78-94fe-4689-907b-c3ae662d0ae3","resolution":{"observed_at":"2026-08-11T19:14:20.230862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.478269Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":"68e6eea7-28d9-4012-8f88-f17dceb8f98c","year":2019},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.234862Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:5555bb785e9e9e6c7a376cf10638255a49026c830fdfa6ce78834a856ce3c3b7","observation_id":"a79ff762-2bcc-4b45-bec6-23a031968226","resolution":{"observed_at":"2026-08-11T19:14:20.483374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T19:14:20.238947Z","title":"Adam: a method for stochastic optimization (2014),","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.238947Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:8853753fde1c751c2f1ddfefa871a98dcb436af2dae4f9f25073a086ce40d4e7","observation_id":"963f694c-200b-4de9-9ff1-7a6f7ba311a6","resolution":{"observed_at":"2026-08-11T19:14:20.238947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.243081Z","title":"Fusing event-based and rgb camera for robust object detection in adverse conditions,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.243081Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:9f570ce208d25f256a2e7fbc68ae5191f19325f5135891f389125704d24ea9fa","observation_id":"17c14f19-21c2-4df9-8e63-4c2af965e3e8","resolution":{"observed_at":"2026-08-11T19:14:20.243081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.458237Z","title":"Self-supervised learning of event-based optical flow with spiking neural networks,","venue":null,"work_id":"a52983d1-84c9-4977-86e1-bb4e8d8b285b","year":2021},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.246840Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:af78d106ebda00c33654d0ce29a750fde1134813ca707b1808a5707b401fd3c4","observation_id":"971ee46d-90df-4b6b-9551-6441e35f9d69","resolution":{"observed_at":"2026-08-11T19:14:20.462542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.446112Z","title":"Spike-flownet: event-based optical flow estimation with energy- efficient hybrid neural networks,","venue":null,"work_id":"b4a78403-42e6-4abf-97b8-b69bbe58ae92","year":2020},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.250560Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:86e95e3056f8d468751feed164cc0f1ba2d4ba47e93d5385b48118c654e5b1c2","observation_id":"579c8477-c8ce-4ed2-bffb-2dc172a62fba","resolution":{"observed_at":"2026-08-11T19:14:20.450170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.09378","last_updated":"2020-12-17T03:16:13Z","snapshot_observed_at":"2026-08-13T20:44:17.302496Z","submitted_at":"2020-12-17T03:16:13Z","title":"Event Camera Calibration of Per-pixel Biased Contrast Threshold","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.09378","snapshot_observed_at":"2026-08-11T19:14:20.254363Z","title":"Event camera calibration of per-pixel biased contrast threshold,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.254363Z"},"links":{"cited_paper":"/paper/2012.09378","citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:1bbdde2de0e465e1584b2afb524ae03f15236d374dceeef6373412efef5f4134","observation_id":"a82db979-b209-41a8-bccc-a91dc404396b","resolution":{"observed_at":"2026-08-11T19:14:20.254363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.432829Z","title":"Real-time, high-speed video decompression using a frame-and event-based davis sensor,","venue":null,"work_id":"811dc196-d634-4c60-b9e9-1063443d9286","year":2014},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.258435Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:c2782edee4ee25afa823cea08c5b27c2617d17ba5e7575c52c1f3284a756e9ed","observation_id":"52bb4ef5-00f2-49fb-bec5-37b7f85bb9e9","resolution":{"observed_at":"2026-08-11T19:14:20.437036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:14:20.418149Z","title":"A dynamic vision sensor with 1% temporal contrast sensitivity and in-pixel asynchronous delta modulator for event encoding,","venue":null,"work_id":"fdee4b72-edd8-421e-90f8-d14c4cc6683a","year":2015},"citing_paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T19:14:20.262245Z"},"links":{"citing_paper":"/paper/2412.07080"},"observation_digest":"sha256:8411e3a4f01c88c51a9272e614e5d8f3d7a677a8c7b796541ab82860d014468b","observation_id":"6dae32fb-bf28-4459-81d2-d896a777a7c4","resolution":{"observed_at":"2026-08-11T19:14:20.423984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.07080","last_updated":"2024-12-10T00:42:54Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T15:08:51.792647Z","submitted_at":"2024-12-10T00:42:54Z","title":"EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision"},"reference_resolution":{"displayed":81,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":0,"verified_fuzzy":50},"total_outbound_references":81},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 0 inbound Pith citation observations for arXiv:2412.07080."}