{"as_of":"2026-08-16T10:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fb8813e11beb26001b2472381109b9999e4f5d7a4f00c43bd4305a2356c61e84","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:50:33.347244Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.06391/citation-record","integrity":"/paper/1908.06391/integrity","json":"/paper/1908.06391/citation-record.json","paper":"/paper/1908.06391"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:50:33.847299Z","title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","venue":null,"work_id":"61893525-4450-4bdc-8a1b-15f983445faf","year":2017},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.180139Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:38a7de635f000bc731a5a39e7066be238fda40ab124e354d3a3032fbb20f3649","observation_id":"33e0144b-fbd1-488c-88c7-245f0af3e044","resolution":{"observed_at":"2026-08-14T12:50:33.852161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.189689Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.189689Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:99b427e667f9e9b6514813f68c99c787a6c4e665691eaf3d8c9d9ef57a3d1863","observation_id":"b6870d29-102f-40de-9650-fd44e1549797","resolution":{"observed_at":"2026-08-14T12:50:33.189689Z","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":"2026-08-14T12:50:33.819784Z","title":"Boxsup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation","venue":null,"work_id":"d65f052e-3841-4f72-b8e3-c004fff99364","year":2015},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.196061Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:921097020a5aa41b82c03e9481efec69ce0876ea1a272d63a3aa04cf50929171","observation_id":"e8c2ad64-11a2-4060-8ee6-71d3185eba94","resolution":{"observed_at":"2026-08-14T12:50:33.825106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.802910Z","title":"Few-shot semantic segmen- tation with prototype learning","venue":null,"work_id":"16e1c47a-e194-40c1-bc53-54a5848d8edb","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.204276Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:22e08f77b9d4ed4344892571b292df84cce352147f7530c10bb2998e0d1691d5","observation_id":"559bebe1-4563-41ed-a692-63f5834b61c0","resolution":{"observed_at":"2026-08-14T12:50:33.808215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.210138Z","title":"The pascal visual object classes (voc) challenge","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.210138Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:9d0478c1f855a67c527e645f3cc3f8e7dc9bd6080f5829b04888bca38c2fe2bf","observation_id":"46e72250-dc51-4513-89c5-1059a68c18b9","resolution":{"observed_at":"2026-08-14T12:50:33.210138Z","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":"2026-08-14T12:50:33.771072Z","title":"Model- agnostic meta-learning for fast adaptation of deep networks","venue":null,"work_id":"4fd91d7c-6d63-404c-84cd-fbaf273ed94f","year":2017},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.216806Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:cbc8f56182f15b8fa5d5363e8c4a3c682c4ed999edd41fff5f5002391c7a70d2","observation_id":"f26607a4-b7af-4d60-bf2a-07c2bb740652","resolution":{"observed_at":"2026-08-14T12:50:33.777269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.753176Z","title":"Semantic contours from inverse detectors","venue":null,"work_id":"5b4fbc19-0dc4-4660-b169-1c958f322dc6","year":2011},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.222432Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:a040d6ba217a09743f9b3d343bb65bd57a343c909f69bf4924a45636cbae906d","observation_id":"b74f2cf8-fc05-46fc-bd51-b59c9a2faac5","resolution":{"observed_at":"2026-08-14T12:50:33.760046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.733740Z","title":null,"venue":null,"work_id":"780830e2-6ff1-4d77-b247-d2ffb6250973","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.228455Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:08400aae8d731c3f890a3b2d0771f33b3b28b87c9ef6761b2868e83d333fe698","observation_id":"d493f01f-f1cd-48b4-8df2-494875d881cb","resolution":{"observed_at":"2026-08-14T12:50:33.739251Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.718105Z","title":"Scribblesup: Scribble-supervised convolutional networks for semantic segmentation","venue":null,"work_id":"9e9eb861-fced-4d48-b958-c5e9df9bc626","year":2016},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.236285Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:64ab5e74f8377919250a5ec94234bafa26cdd3f52934bbd62cad9942d1fbd245","observation_id":"27992f77-aa3a-4e85-8ffb-a24ccb4e0acf","resolution":{"observed_at":"2026-08-14T12:50:33.723284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.242503Z","title":"Refinenet: Multi-path refinement networks for high- resolution semantic segmentation","venue":null,"work_id":null,"year":1925},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.242503Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:89dc291f6c7b5d998f4ddc40004e1d7cf275078730fbdb4260b72a3fcf14f97b","observation_id":"c4fe29ff-218e-468b-a94e-3fa352e12582","resolution":{"observed_at":"2026-08-14T12:50:33.242503Z","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":"2026-08-14T12:50:33.248985Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.248985Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:5cd6e2573d8818933560f1686c0ccc991748ecd982ecfbd595a626afe956047e","observation_id":"01c6ebb1-31b5-458e-b0e1-adc6450c52cf","resolution":{"observed_at":"2026-08-14T12:50:33.248985Z","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":"2026-08-14T12:50:33.676015Z","title":"Learning to propagate labels: Transductive propagation network for few-shot learn- ing","venue":null,"work_id":"1e2733d0-5901-4512-a1aa-daff1c2b05c3","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.255013Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:6f9d53ab229aece2bf503e651d33bfe831c7d879b75484627310fe950869969f","observation_id":"ec303c50-4ba8-4027-819b-4a97a9b6d5bc","resolution":{"observed_at":"2026-08-14T12:50:33.680515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.260717Z","title":"Fully convolutional networks for semantic segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.260717Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:32827e24f3b61c3c4da5faa720f21f1e451bed576ab2663d4ec9bf03a800cac9","observation_id":"64d7ada7-0bec-4322-9213-a7f0148b99da","resolution":{"observed_at":"2026-08-14T12:50:33.260717Z","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":"2026-08-14T12:50:33.650930Z","title":"Tadam: Task dependent adaptive metric for improved few-shot learning","venue":null,"work_id":"6cd66fc8-403b-4165-8a78-eff3f3d2b278","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.265922Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:944b57300b4175724947ec8f9c5dec95b502435e6c91d6460f837e8472a85ab1","observation_id":"7711f37c-0ebb-451a-b50e-95282f3d9c54","resolution":{"observed_at":"2026-08-14T12:50:33.656023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.633032Z","title":"Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmenta- tion","venue":null,"work_id":"604e51cc-205d-463f-9f8d-618d85a4f5ed","year":2015},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.272000Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:0991a4961fbb71c759093ee8ecfd2cb98b8b29ab406d89e02033c343de8ae0b3","observation_id":"198f9d9c-dac7-4f4f-855b-f7e49bc59558","resolution":{"observed_at":"2026-08-14T12:50:33.638865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.615957Z","title":"Conditional networks for few-shot semantic segmentation","venue":null,"work_id":"2f97aab2-f7e5-4787-a131-483c3f5a9eeb","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.278127Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:513380f2f24f9525d57a6b52ba56f5c52085e41854af9f8ee21a334073e67efa","observation_id":"5d6708f2-08d2-4909-9669-d9940efefaef","resolution":{"observed_at":"2026-08-14T12:50:33.621606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07373","last_updated":"2018-05-25T17:02:03Z","snapshot_observed_at":"2026-08-14T19:11:03.367113Z","submitted_at":"2018-05-25T17:02:03Z","title":"Few-Shot Segmentation Propagation with Guided Networks","version":1},"cited_work":{"arxiv_id":"1806.07373","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.07373","snapshot_observed_at":"2026-08-14T12:50:33.472267Z","title":"Few-Shot Segmentation Propagation with Guided Networks","venue":"cs.CV","work_id":"56538133-ac34-4e4e-ab75-3eef1fda9b77","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.283311Z"},"links":{"cited_paper":"/paper/1806.07373","citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:7934ce962168cc1660f0eef92ea591c723b6857b0ee9bbd85b80ebd2c8726847","observation_id":"0bdc9bbf-990b-4e16-a526-75dbaf5538a6","resolution":{"observed_at":"2026-08-14T12:50:33.478613Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.288795Z","title":"Optimization as a model for few-shot learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.288795Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:1f83571f1d1b6b3d136adc80959a5bf2963c6fe12196f48caeca640ace292922","observation_id":"e624a18e-5fb1-4e2f-869a-3f2edf055045","resolution":{"observed_at":"2026-08-14T12:50:33.288795Z","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":"2026-08-14T12:50:33.293959Z","title":"Berg, and Li Fei-Fei","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.293959Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:c140d3299642323592765e93f44ef0c2846a742081dea9597590724ad65d6766","observation_id":"49094711-ef92-45ae-8e7b-65ae247213f0","resolution":{"observed_at":"2026-08-14T12:50:33.293959Z","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":"2026-08-14T12:50:33.577649Z","title":"Few-shot learning with graph neural networks","venue":null,"work_id":"8b84be57-62d9-4cde-96f6-13884506e1cf","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.300597Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:029292768b2d5c5aeaa2fd5c76c6810836257aba72f9fda9922bfbe9e99541ba","observation_id":"616d5e89-4c4a-4112-93fa-67040f14552a","resolution":{"observed_at":"2026-08-14T12:50:33.582836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.03410","last_updated":"2017-09-11T14:34:58Z","snapshot_observed_at":"2026-08-14T20:34:21.863331Z","submitted_at":"2017-09-11T14:34:58Z","title":"One-Shot Learning for Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.03410","snapshot_observed_at":"2026-08-14T12:50:33.306275Z","title":"One-shot learning for semantic segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.306275Z"},"links":{"cited_paper":"/paper/1709.03410","citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:26400a4948e218130e5fed7ee41b89d94180e2a176555f7fc953bded3059634f","observation_id":"affdcbf3-e8a2-4a03-adf7-9e307894afe7","resolution":{"observed_at":"2026-08-14T12:50:33.306275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T23:20:42.336514Z","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":"2026-08-14T12:50:33.311633Z","title":"Very deep convo- lutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.311633Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:306f2448b4c15102d648a2929ed8bbed6db008c171d1eb179ef1c05ef40beb9e","observation_id":"ac8d43d7-c5d7-4055-a4d4-3e1d93c25106","resolution":{"observed_at":"2026-08-14T12:50:33.311633Z","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":"2026-08-14T12:50:33.560537Z","title":"Prototypi- cal networks for few-shot learning","venue":null,"work_id":"9ad8f5fa-2caa-4daa-8180-ecfece3bcb2f","year":2017},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.317004Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:ab1dac5c2bd6b35a5c374d491fd86fb1f1c8fc0945b8adafa4a73b29d197bd2a","observation_id":"f05e688c-f727-453b-b236-64944bf4ee28","resolution":{"observed_at":"2026-08-14T12:50:33.566096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.543322Z","title":"Learning to compare: Re- lation network for few-shot learning","venue":null,"work_id":"48967aec-8ce8-4382-94e1-caa604162938","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.322206Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:a34b961d9d44e17fedbe74e85508f712ed00957ca41cd5d016fc1cacce0054e7","observation_id":"722bbe4b-e77f-4e30-ba9f-e94b3e051b8f","resolution":{"observed_at":"2026-08-14T12:50:33.548967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.327931Z","title":"Matching networks for one shot learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.327931Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:90182075ac37218f102061a31cefff9dace352cf744d8d2aac0c4282b1b185a3","observation_id":"4875a911-4767-4f4d-b927-47768ac76927","resolution":{"observed_at":"2026-08-14T12:50:33.327931Z","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":"2026-08-14T12:50:33.514547Z","title":"Object region mining with adversarial erasing: A simple classification to semantic segmentation approach","venue":null,"work_id":"766560cb-3767-45d8-be9f-e743f7891c5a","year":2017},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.332742Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:39f3f311bf7f4c1e0b68e389369b961a662f5fc9c0b583760b14e1081dce7a10","observation_id":"6a27c5a8-afff-4b9c-a2c7-d254b9600ded","resolution":{"observed_at":"2026-08-14T12:50:33.519320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.07122","last_updated":"2016-04-30T18:19:37Z","snapshot_observed_at":"2026-08-14T22:21:48.435559Z","submitted_at":"2015-11-23T07:32:14Z","title":"Multi-Scale Context Aggregation by Dilated Convolutions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.07122","snapshot_observed_at":"2026-08-14T12:50:33.337066Z","title":"Multi-scale context aggregation by dilated convolutions","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.337066Z"},"links":{"cited_paper":"/paper/1511.07122","citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:bd55aec6f8ed30706dfa76b7f8c2c961d9ff6d013bd42598ca5c87a6280be1a4","observation_id":"1933497a-e090-43d3-8070-3afebac04cd2","resolution":{"observed_at":"2026-08-14T12:50:33.337066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.09091","last_updated":"2020-05-12T11:34:33Z","snapshot_observed_at":"2026-08-14T18:11:26.718739Z","submitted_at":"2018-10-22T05:30:04Z","title":"SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation","version":4},"cited_work":{"arxiv_id":"1810.09091","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.09091","snapshot_observed_at":"2026-08-14T12:50:33.390195Z","title":"SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation","venue":"cs.CV","work_id":"46d95ae3-1416-464a-b7c8-a2c00564ecf4","year":2018},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.341603Z"},"links":{"cited_paper":"/paper/1810.09091","citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:9532de36f9b69f069258659b014e266b196aad605e6adff260e04c9fa10a03df","observation_id":"7c378d53-6244-47d3-90c8-6d2ec1c7dee4","resolution":{"observed_at":"2026-08-14T12:50:33.398306Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:50:33.347244Z","title":"Pyramid scene parsing network","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T12:50:33.347244Z"},"links":{"citing_paper":"/paper/1908.06391"},"observation_digest":"sha256:43992f14c660ba5a557dd3caf348e635ffb27a79482dfc7595c6b81e80cb5541","observation_id":"84d22b4a-568d-4d93-b505-f91e1b33b334","resolution":{"observed_at":"2026-08-14T12:50:33.347244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.06391","last_updated":"2020-02-07T03:20:01Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T06:30:23.514381Z","submitted_at":"2019-08-18T07:56:19Z","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":2,"verified_fuzzy":14},"total_outbound_references":29},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:1908.06391."}