{"as_of":"2026-08-18T02:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:21661fef9b0d334e6c4053feb5a263754756a8dc4f005568fc8226bc45407ca8","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:36:51.614563Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.12547/citation-record","integrity":"/paper/2505.12547/integrity","json":"/paper/2505.12547/citation-record.json","paper":"/paper/2505.12547"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:36:52.406080Z","title":null,"venue":null,"work_id":"292244ca-16fb-485e-8646-6da6f94bfc9a","year":2021},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.372190Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:a5d9bfbb205d29dc47850cf55471893fdb850ebc5dd63bcca25ae5fd39525e50","observation_id":"8bf2686a-5832-47cb-a992-2274bc9d6496","resolution":{"observed_at":"2026-08-15T20:36:52.410629Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.389915Z","title":"Prototype as query for few shot semantic segmentation","venue":null,"work_id":"78c7cc96-24c6-4fa1-8468-68b8d9be60c7","year":2024},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.377761Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:d9566bb6eaa27688b1bb69b444faaa9608a432d257444dc7cb0abb9762e1136e","observation_id":"fcb24603-5534-4fa9-8df5-341368e12b3e","resolution":{"observed_at":"2026-08-15T20:36:52.395689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.373657Z","title":"A closer look at few-shot classification","venue":null,"work_id":"aea45e37-95cc-486e-bc01-f04e6e8cb9b2","year":2019},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.382405Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:513fd6c861a1a21cad57c544d197e76a6b1bbc4cb1fd5be2f92a2338fa722fcc","observation_id":"8ebf1038-fe53-48cf-9a32-14cf84f415ac","resolution":{"observed_at":"2026-08-15T20:36:52.379051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.355460Z","title":"Shot in the dark: Few-shot learning with no base-class labels","venue":null,"work_id":"e210e7f6-290f-4244-b989-0c3d5f929067","year":2021},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.387209Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:03ee58c9ebeb19272388f22f29b0b45e73884bedf349ff694e5e2e636c81e7d6","observation_id":"e1664bb6-b8db-49e5-82ee-502bd2380bb3","resolution":{"observed_at":"2026-08-15T20:36:52.361022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.338002Z","title":"Yolo-world: Real-time open-vocabulary object detec- tion","venue":null,"work_id":"aaec787f-d9de-4baa-877d-37b625c42716","year":2024},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.391912Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:5559009f201f34d3e2709a82ab4aee2c9f040c0f820ff1c7c13e6ca0fd0d0d38","observation_id":"ef2b950b-9258-4e61-93fc-d89500054c91","resolution":{"observed_at":"2026-08-15T20:36:52.343818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.321369Z","title":"The cityscapes dataset for semantic urban scene understanding","venue":null,"work_id":"46c14b09-3720-43b8-b207-cd6e392e3b0f","year":2016},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.397031Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:b90b671f011c1976bc827ef88833ebae8f45d3b39a888744baa9149da983186d","observation_id":"0a188f28-6c4f-4f72-84ac-735ea51f6184","resolution":{"observed_at":"2026-08-15T20:36:52.326683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.305960Z","title":"Maximum likelihood from incomplete data via the em algorithm","venue":null,"work_id":"fb8039d5-9684-4363-9acb-d31402c5e622","year":1977},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.403254Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:0eb0775a16ec219ae8b9cc8206569a1c9d403bf4e07c5db50478894082519dec","observation_id":"19ce673f-4184-4e1c-88d4-1dba95ecbcab","resolution":{"observed_at":"2026-08-15T20:36:52.311175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.290943Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":"deba31e2-6aa7-40ff-9487-abca4cc38736","year":2021},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.407505Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:e6561aab3fb0c9fd428fe5f59f0434451d1a74aaea20c703b49799e135ddd703","observation_id":"a1d08864-a528-4b7f-afc6-0fbdbc339e87","resolution":{"observed_at":"2026-08-15T20:36:52.295820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.411979Z","title":"The pascal visual object classes (voc) challenge","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.411979Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:a5bb3aab7a0e66d44705fea39880381ed3d545848b5344f34996862d7beab454","observation_id":"24cbba59-d151-41cb-8405-b790dc77ea9b","resolution":{"observed_at":"2026-08-15T20:36:51.411979Z","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":"2026-08-15T20:36:51.416295Z","title":"Model-agnostic meta-learning for fast adaptation of deep networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.416295Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:f9a439b3406189abaaa7c1a0e20ff901347c1f877746ec36b465fb117e07bb20","observation_id":"98cae1b4-78f0-467b-a683-c89a944bf208","resolution":{"observed_at":"2026-08-15T20:36:51.416295Z","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":"2026-08-15T20:36:52.256148Z","title":"Learning few-shot segmentation from bounding box annotations","venue":null,"work_id":"8613a47e-a8f9-4d4a-899c-0ca03ed867bc","year":2023},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.420737Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:da4fd67597338d31897ffd34f57d96653980ad85eb94774dd7328b8b77a35a9b","observation_id":"25869617-0e21-4cc5-bb0c-6b5627e1bc9f","resolution":{"observed_at":"2026-08-15T20:36:52.261003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.240972Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","venue":null,"work_id":"8ac11315-939c-4b29-8bd4-ccbaa1c0d847","year":2015},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.424957Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:6c465635b7a1a5435c21dfd90d1ebde63b7baf92df17e1a3de888eea8305bb71","observation_id":"2290c85e-b5f8-4874-83e7-2e1b368552ce","resolution":{"observed_at":"2026-08-15T20:36:52.245798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.429377Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.429377Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:331d4657d3bb375f73fdae523ffa3f552384fc5767f8895250f75e02847d01c6","observation_id":"1a4f6aee-6e97-45d4-93db-1881d84a3966","resolution":{"observed_at":"2026-08-15T20:36:51.429377Z","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":"2026-08-15T20:36:52.213663Z","title":"Semantic segmentation of underwater imagery: Dataset and bench- mark","venue":null,"work_id":"163384fa-dfa7-437f-b088-dcbe95d17455","year":2020},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.433714Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:f18313b7ec5747c9142b36514d4a5a7cf483a281a84fb8daf575542cc847d6d4","observation_id":"8429226f-b3ed-4182-b81d-d6d85962698d","resolution":{"observed_at":"2026-08-15T20:36:52.219019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.197086Z","title":"Distilling self-supervised vision transformers for weakly-supervised few-shot classification & segmentation","venue":null,"work_id":"ba63fd98-da14-4c97-9237-548a1ab21459","year":2023},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.438486Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:235712a6d9e5b1110dca71afd26e671f441ad2e89d53cf30d6f7f54ac1c00032","observation_id":"ef22c547-ba0c-41e9-98e6-63701b76a299","resolution":{"observed_at":"2026-08-15T20:36:52.202576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.180434Z","title":"Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross B","venue":null,"work_id":"0492bf07-ca73-41fb-898f-45baebd97b44","year":2023},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.443143Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:464337d7f97d4a0708a87527f200654e4ca89be7ec1bb51582ab14444650f984","observation_id":"c94c8020-c273-4ae9-90f3-adbbd100db7f","resolution":{"observed_at":"2026-08-15T20:36:52.186006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.447749Z","title":"Imagenet classification with deep convolutional neural networks","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.447749Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:fdfda9831dfda3c1865ad8b8992ec54272ed8be0a69a8e2a50d063992de9be24","observation_id":"cd1ab817-7d49-4c75-b082-38e40087c868","resolution":{"observed_at":"2026-08-15T20:36:51.447749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.09903","last_updated":"2022-05-30T12:28:14Z","snapshot_observed_at":"2026-08-16T17:05:37.858230Z","submitted_at":"2022-04-21T06:21:14Z","title":"Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.09903","snapshot_observed_at":"2026-08-15T20:36:51.451951Z","title":"Beyond the prototype: Divide-and-conquer proxies for few-shot segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.451951Z"},"links":{"cited_paper":"/paper/2204.09903","citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:ec37919581f7163315eca1350ac369c09fb32414cfe873d6440bdab4ffa52e5b","observation_id":"94ea2462-4802-4097-8fbe-acf0680f3de2","resolution":{"observed_at":"2026-08-15T20:36:51.451951Z","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":"2026-08-15T20:36:52.152020Z","title":"Mi- crosoft coco: Common objects in context","venue":null,"work_id":"388dbfa8-336e-4535-847d-5694430a3f18","year":2014},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.456760Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:617cb1fa9b396d90a9d9aaacd525578ba10a6d556b20518f0e9513b20c0c8eaa","observation_id":"fe0af4cd-38de-4bcc-8f9a-a292f68d5824","resolution":{"observed_at":"2026-08-15T20:36:52.157962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.136706Z","title":"Dynamic prototype convolution network for few- shot semantic segmentation","venue":null,"work_id":"b7268071-66cf-46e6-83aa-53da379dbdbd","year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.460828Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:c28b4921ca8d71715e8292d96614b68a240d764b349e6301dd2a76942d1f031f","observation_id":"ab317082-49ef-49b5-93a5-5e96ec255c6b","resolution":{"observed_at":"2026-08-15T20:36:52.141638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.119784Z","title":"Prototype rectification for few-shot learning","venue":null,"work_id":"24f195ea-5ada-49aa-8c32-ffc8345b2751","year":2020},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.465327Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:ffb5bf01fce8731d764b03aeba7e88dae70b4087913f85dcd8491a3b0726ada9","observation_id":"362eacef-6923-4173-84e4-f861ea246728","resolution":{"observed_at":"2026-08-15T20:36:52.126077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.104352Z","title":"Learning non-target knowledge for few-shot semantic seg- mentation","venue":null,"work_id":"84540420-3511-437f-bab7-b14b7f228597","year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.469976Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:6c56670dff39da267ae597eff4a4ae69b1ed641ac8b5c964530713c720ee4148","observation_id":"3938bce7-858f-43c9-8cfb-7e19d5c36eb6","resolution":{"observed_at":"2026-08-15T20:36:52.109351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.088970Z","title":"Intermediate prototype mining transformer for few-shot semantic segmentation","venue":null,"work_id":"32678bcb-f741-4b9f-860c-cf3146995927","year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.474524Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:1186d4d4022b0e47f167000354c5a85aa0b6e1a90b5dc1f749ac02031fe4b268","observation_id":"597538bc-7cb0-4f41-9273-f800d06a0795","resolution":{"observed_at":"2026-08-15T20:36:52.093834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.479322Z","title":"Least squares quantization in pcm","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.479322Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:8642c67874738a23ac05ee901aff6d31b8287ec0714d92ac316bd5d7bcbb97ce","observation_id":"65bca6fe-ba54-427c-b457-01b553138085","resolution":{"observed_at":"2026-08-15T20:36:51.479322Z","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":"2026-08-15T20:36:52.063387Z","title":"Uavid: A semantic segmentation dataset for uav imagery","venue":null,"work_id":"b5bf7ecf-579d-49bf-b8a7-31fe04c64d20","year":2020},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.483838Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:799bb43a5289ec91cb25f657655d8b82e47499911a28721fe13c59f60241429b","observation_id":"4c03ef14-d9c6-4370-883e-f8154d86a529","resolution":{"observed_at":"2026-08-15T20:36:52.068073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.045820Z","title":"Some methods for classification and analysis of multivariate observations","venue":null,"work_id":"cd62cec5-54a4-4046-978d-ec91e9f37334","year":1967},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.488355Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:4a244c9121ca2d2115c44be8bff361715d02430707eae548e8d0a8e59e4a0716","observation_id":"f962001f-6eda-42aa-9587-7890cb6e927d","resolution":{"observed_at":"2026-08-15T20:36:52.051783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.03271","last_updated":"2023-02-16T16:54:58Z","snapshot_observed_at":"2026-08-16T16:54:45.322135Z","submitted_at":"2022-06-07T13:24:00Z","title":"On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2206.03271","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.03271","snapshot_observed_at":"2026-08-15T20:36:51.700832Z","title":"On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning","venue":"cs.LG","work_id":"b7ce3e47-4064-4568-b61d-bc6240ac155e","year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.493272Z"},"links":{"cited_paper":"/paper/2206.03271","citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:03d91efa0b59f0df947072ea6ff2e3c242ef49a52d1271d29851ba475ffe5d21","observation_id":"96f14af1-6851-4909-9c18-d2d5243cc46a","resolution":{"observed_at":"2026-08-15T20:36:51.708078Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.029205Z","title":null,"venue":null,"work_id":"25ca80f7-d252-4f0b-a388-e2182f547ed2","year":2015},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.499326Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:ea026d26055509b91b66601de07152efd753a483e2f76f435698870cb8c39326","observation_id":"b9446356-3ef8-4cc8-903b-c7cd4cf898ab","resolution":{"observed_at":"2026-08-15T20:36:52.034065Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:52.013596Z","title":"Interclass prototype relation for few-shot segmen- tation","venue":null,"work_id":"56023d49-3b2d-4fd5-b801-dba530dc4a3c","year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.504337Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:78d974844e3a7098fd6d0ef0b85703a6b85b2e3b69c9884b8644e603e8737059","observation_id":"dd210ed2-503e-40e5-9d49-beae348cc123","resolution":{"observed_at":"2026-08-15T20:36:52.018695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.998005Z","title":null,"venue":null,"work_id":"90c9ba87-775e-4035-8b05-9deb3f7d5f11","year":2024},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.509553Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:63c83e11b5c8fecc8c9580ce824172256523097388a45cd44a54617975a87fa5","observation_id":"5d38cb6e-68bf-48ac-9ccb-b193527b1d66","resolution":{"observed_at":"2026-08-15T20:36:52.002827Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.982553Z","title":"In- telligent robotic perception systems","venue":null,"work_id":"85dcae61-e6f6-4f8e-ae90-71902c61f2a9","year":2018},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.514682Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:21e75bf8b80d3102e40f45e45def1e3cbf901662914bc666ceca8a00a2cfff71","observation_id":"baced79b-2595-4dc5-8e4a-50de594b994b","resolution":{"observed_at":"2026-08-15T20:36:51.987423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.967094Z","title":"Weakly supervised one shot segmentation","venue":null,"work_id":"b80e9dcb-568f-4ab0-8ee9-f27091cb5c42","year":2019},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.519612Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:26d93cc0679e4aba44aaafe2391ded903d63d695d2635d0c4718b39959d06726","observation_id":"45971cda-b318-4d4e-a9ce-c4f25ee03a6c","resolution":{"observed_at":"2026-08-15T20:36:51.971754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.524444Z","title":"You only look once: Unified, real-time object detection","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.524444Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:830444f170100107a58f2b142fdc093abf915ea3c482839499942b13f33d45c2","observation_id":"7433da92-e192-404e-8f97-b1b4b0810e4d","resolution":{"observed_at":"2026-08-15T20:36:51.524444Z","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":"2026-08-15T20:36:51.941231Z","title":"Girshick, and Jian Sun","venue":null,"work_id":"1e2f34ce-54f2-4765-ad20-b0039e3054d2","year":2015},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.529397Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:7a7844bb8aadbe60fe735a5213d0f8ce0c839ac08a2808f82fdbef34b8d374e3","observation_id":"6d560c73-fa66-4d01-b035-936555e9c926","resolution":{"observed_at":"2026-08-15T20:36:51.946154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.09540","last_updated":"2020-05-17T17:08:51Z","snapshot_observed_at":"2026-08-16T19:12:20.606225Z","submitted_at":"2020-01-26T23:56:26Z","title":"Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.09540","snapshot_observed_at":"2026-08-15T20:36:51.534478Z","title":"Weakly supervised few-shot object segmentation using co-attention with visual and semantic embeddings","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.534478Z"},"links":{"cited_paper":"/paper/2001.09540","citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:370fe0e872a2527e1f955338c2c3bee14548117c318e7dfd6e8501e1038157f0","observation_id":"4481899b-c04c-42fe-a0a9-51644f306e81","resolution":{"observed_at":"2026-08-15T20:36:51.534478Z","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":"2026-08-17T19:17:06.411141Z","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":"2026-08-15T20:36:51.539745Z","title":"Very deep convolu- tional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.539745Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:c089bd6733b646a8083e34dac2594fce61b4f76c73b979dc67a9dbccc601a6bf","observation_id":"bae7b02c-19d7-4358-9233-31989bc886ab","resolution":{"observed_at":"2026-08-15T20:36:51.539745Z","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":"2026-08-15T20:36:51.544639Z","title":"Prototypical networks for few-shot learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.544639Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:44e0915119b40f2c90888d7a15f630a068bdec5b3573b1bd171446e7821c39e2","observation_id":"cd09d61d-de5e-4fcc-b36e-f76577b6ab2b","resolution":{"observed_at":"2026-08-15T20:36:51.544639Z","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":"2026-08-15T20:36:51.915457Z","title":"Learning to compare: Relation network for few-shot learning","venue":null,"work_id":"bd8e3f6e-3cb8-43ad-bb89-b36965166b47","year":2018},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.549682Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:8cbbf86479b83e38f3fa926b32fe0fffebdc160a957b8dfd7522e0c2062b1da1","observation_id":"6065030a-62a0-4ff7-aa92-6c40730c24b0","resolution":{"observed_at":"2026-08-15T20:36:51.920585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.899864Z","title":null,"venue":null,"work_id":"59db2ecf-2103-4543-847e-787236194ded","year":2020},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.554679Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:97bfe111e07998a43395f68b1b3fbf53a6234124c741f5a8df32392fd5b4c786","observation_id":"9938e1a0-763f-429a-97c3-92c9ba8fcc7e","resolution":{"observed_at":"2026-08-15T20:36:51.904791Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.884033Z","title":"Spin: Simultaneous perception interaction and naviga- tion","venue":null,"work_id":"463d85fb-0b64-4c35-9ef1-7a8b42671601","year":2024},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.559783Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:261811c814be7a1323b720f7cd9df4ecf9b8c162ff4e453371efa9754d50f225","observation_id":"df5d353f-5936-4dbf-8053-e85ae57f7e4b","resolution":{"observed_at":"2026-08-15T20:36:51.888977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.564639Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.564639Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:21c255c3a76b4109d4f67ee6ad1a6f166238dfba1ea618d28b9f3b56a686594f","observation_id":"5fb11c8a-b724-4ef8-89b4-2e5f3944e5b2","resolution":{"observed_at":"2026-08-15T20:36:51.564639Z","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":"2026-08-15T20:36:51.857477Z","title":"Panet: Few-shot image semantic segmentation with prototype alignment","venue":null,"work_id":"5b4a7d3e-a646-48c2-b71a-2c207b9cb069","year":2019},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.569395Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:a25c3f7f490aab678ef32c19ed25d074f85c68901071ebaf5160738b81352b84","observation_id":"7b73f39a-079c-4700-a9fa-b9fca5a11573","resolution":{"observed_at":"2026-08-15T20:36:51.862361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.04623","last_updated":"2019-11-16T00:35:54Z","snapshot_observed_at":"2026-08-14T22:22:39.515577Z","submitted_at":"2019-11-12T00:44:10Z","title":"SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.04623","snapshot_observed_at":"2026-08-15T20:36:51.574308Z","title":"Simpleshot: Revisiting nearest-neighbor classification for few-shot learning","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.574308Z"},"links":{"cited_paper":"/paper/1911.04623","citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:c5cab7653dd3cf63bb8e961f3dbcac2a4f1d0eaebae0eeaac6d0589b2d6c2ffc","observation_id":"fab2c943-0200-4b26-beb8-0fd6bfabca2a","resolution":{"observed_at":"2026-08-15T20:36:51.574308Z","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":"2026-08-15T20:36:51.841966Z","title":"Adaptive agent transformer for few-shot segmentation","venue":null,"work_id":"8eefaf03-9739-4f60-bab5-dc5cd2d91332","year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.579803Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:d0bc5f7fcf12eaa08cf1d61218dbd79f83a12e0b8f07282ed71a9962ac3a2617","observation_id":"7b8e3918-9057-4650-935b-97b4d43b2c6b","resolution":{"observed_at":"2026-08-15T20:36:51.847213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.825255Z","title":"Prototype mixture models for few-shot semantic segmentation","venue":null,"work_id":"850f7482-7309-4f3d-9ee5-71adcb594aa5","year":2020},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.585021Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:6fbde097d8a91047ee912370d3a4d754afca121bc49d2101145ac8ca52c25811","observation_id":"32453d13-3dbc-4f81-8922-8f2f0f498a37","resolution":{"observed_at":"2026-08-15T20:36:51.830679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.808321Z","title":"Self-guided and cross- guided learning for few-shot segmentation","venue":null,"work_id":"31bf9930-58f7-4ec5-9ad1-ccd70fcd7f2e","year":2021},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.590191Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:3eaf620ad3e0399ec0fdc4dd878832098e9e04ca2fab51be28abb3cd85ead5e0","observation_id":"ed16f92c-aab3-4eaf-8352-ce4d64f1520a","resolution":{"observed_at":"2026-08-15T20:36:51.813325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.792372Z","title":"Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning","venue":null,"work_id":"42e860c4-0779-4782-8ea5-d63504efb280","year":2019},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.595188Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:11cf17503cfa769e5e4f88b3a8fe5af9da1b0a5e31f1aae6d1469b2993360199","observation_id":"28365067-164b-4f7a-8a08-0db7f8284452","resolution":{"observed_at":"2026-08-15T20:36:51.797379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.776893Z","title":"Mask matching transformer for few-shot segmentation","venue":null,"work_id":"ac86b1b1-ff32-4ca7-b453-f1ce0e8a5324","year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.600260Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:9f8852389dd0903f8b8cb4e35bc86c2ba678b51c6b4c9f07ebd5374157ebdc8f","observation_id":"763c1b73-33cf-4133-b835-2417952905d6","resolution":{"observed_at":"2026-08-15T20:36:51.781713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.761079Z","title":"Feature- proxy transformer for few-shot segmentation","venue":null,"work_id":"45537f81-dd76-4d8c-a970-c56cee38d202","year":2022},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.605043Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:44bda93b67f50cb604e77a49f10614ec13def5e4e8070712b0b65ffaa4f18ba4","observation_id":"3b9389ce-2666-49b1-a956-8e4c4d81541a","resolution":{"observed_at":"2026-08-15T20:36:51.765918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.745306Z","title":"Sg- one: Similarity guidance network for one-shot semantic segmentation","venue":null,"work_id":"c99f8546-8577-4883-abca-4ac7402f9a0c","year":2020},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.609858Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:81d1fb6ad126f27bddf27b901ec923df0007cf2d84f28b2075ffe9ed46a6aef4","observation_id":"cf6f1be0-84e6-464a-b313-3c86239cb62a","resolution":{"observed_at":"2026-08-15T20:36:51.750448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:36:51.614563Z","title":"Pyramid scene parsing network","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T20:36:51.614563Z"},"links":{"citing_paper":"/paper/2505.12547"},"observation_digest":"sha256:c312aca9096d1d63acbfc5f92ea0410060ab006cfd5db49f4bf87e07b18c7014","observation_id":"7eaac1b2-9ade-45af-8589-9e2e660169ce","resolution":{"observed_at":"2026-08-15T20:36:51.614563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.12547","last_updated":"2025-05-18T21:08:05Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T20:29:10.990936Z","submitted_at":"2025-05-18T21:08:05Z","title":"ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":1,"verified_fuzzy":33},"total_outbound_references":51},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2505.12547."}