{"as_of":"2026-08-08T16:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c2f3ad62139f88a60a0b0459cc04aa29194be663e574056b3cb1b949c06b5997","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T04:23:28.681011Z","state":"measured"},{"denominator":73,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":73,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2602.05217/citation-record","integrity":"/paper/2602.05217/integrity","json":"/paper/2602.05217/citation-record.json","paper":"/paper/2602.05217"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:23:17.956008Z","title":"Few-shot seg- mentation without meta-learning: A good transductive infer- ence is all you need? InCVPR, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:17.956008Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:9e42c156d1f6c19a5475ef50a494e6c009fb78a499233dd53077d0d065aeb14f","observation_id":"4da9ba6b-1ccd-44fd-99a5-86af563dffd4","resolution":{"observed_at":"2026-08-03T04:23:17.956008Z","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":"2026-08-03T04:23:18.074894Z","title":"Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration.TMI, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:18.074894Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:fd06d1f6107d1bf628001f08088442b6f2a7cd3ab7bb63949c54fdbfdd8a95da","observation_id":"63bfca29-0146-43a0-aa74-74c8a032d9a7","resolution":{"observed_at":"2026-08-03T04:23:18.074894Z","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":"2026-08-03T04:23:18.274753Z","title":"Pixel matching network for cross-domain few- shot segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:18.274753Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:3df11a6a0ee94eb0b55dcba67386db84029a60f265311074ddd5972a9fd0d234","observation_id":"569efcb9-e421-499c-b0a3-d682e9468e34","resolution":{"observed_at":"2026-08-03T04:23:18.274753Z","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":"2026-08-03T04:23:18.494826Z","title":"Cross-domain few-shot semantic segmentation via doubly matching transformation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:18.494826Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:6bc465d35f546ca0f2cf974e2a398f5e7cebfb0592f98d6aead6798cdab952bb","observation_id":"18ee7e8e-c06e-4515-8ead-c410723676d0","resolution":{"observed_at":"2026-08-03T04:23:18.494826Z","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":"2026-08-03T04:23:18.845285Z","title":"A closer look at few-shot classi- fication","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:18.845285Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:5bb7d771baea40480a47872d80686d256f10fc06a5ef7af744b308c0a9f64f23","observation_id":"6a2bb31e-ff91-408d-aec4-0fd13e3495ce","resolution":{"observed_at":"2026-08-03T04:23:18.845285Z","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":"2026-08-03T04:23:18.992121Z","title":"Holistic pro- totype activation for few-shot segmentation.TPAMI, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:18.992121Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:a9256833d63bc8fadbf89a7a0e17655957a11b6259d913457bc48d1e263425b5","observation_id":"33bbed9f-03ca-40b6-a965-d88e14c3c804","resolution":{"observed_at":"2026-08-03T04:23:18.992121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03368","last_updated":"2019-03-29T17:36:27Z","snapshot_observed_at":"2026-08-07T00:39:52.073704Z","submitted_at":"2019-02-09T04:18:10Z","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.03368","snapshot_observed_at":"2026-08-03T04:23:19.171151Z","title":"Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the interna- tional skin imaging collaboration (isic).arXiv preprint arXiv:1902.03368, 2019","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.171151Z"},"links":{"cited_paper":"/paper/1902.03368","citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:cd4a6160de750996dacc9a61b71213714307356a2e6747c68404421bd3f0dd2a","observation_id":"3c321538-2924-4369-a1fe-69183c1dc9ca","resolution":{"observed_at":"2026-08-03T04:23:19.171151Z","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":"2026-08-03T04:23:19.266270Z","title":"Deepglobe 2018: A challenge to parse the earth through satellite images","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.266270Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:efe832e2d0aea493c635a8cf27ae79f76b10cc922b7021cd28ed1fe1b7dca1ac","observation_id":"fb779170-c30d-43dd-96dc-8dfdcc201022","resolution":{"observed_at":"2026-08-03T04:23:19.266270Z","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":"2026-08-03T04:23:19.414795Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.414795Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:80ceb02a6dd09775cabe079c9b8e7c66aa68e9173c1289c466b4a657aa5a6324","observation_id":"06ef6f77-a9d4-4d18-be66-40355c78cbae","resolution":{"observed_at":"2026-08-03T04:23:19.414795Z","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":"2026-08-03T04:23:19.484502Z","title":"Few-shot semantic segmen- tation with prototype learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.484502Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:12e574452738ce37c27608d8dbe767bd618832f28d4e716b85ed853bd3efaa9a","observation_id":"d8093f6e-56df-4dc1-9422-1c81f90442bb","resolution":{"observed_at":"2026-08-03T04:23:19.484502Z","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":"2026-08-03T04:23:19.574921Z","title":"The pascal visual object classes (voc) challenge.IJCV, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.574921Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:7be79b24e121084030336066b4cc9db39ee3f55a5994ad5035b1071b876357a1","observation_id":"3853d164-c775-40e9-8978-6ed3fe03a21a","resolution":{"observed_at":"2026-08-03T04:23:19.574921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04813","last_updated":"2023-12-08T03:03:22Z","snapshot_observed_at":"2026-07-06T16:58:39.949453Z","submitted_at":"2023-12-08T03:03:22Z","title":"DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04813","snapshot_observed_at":"2026-08-03T04:23:19.674755Z","title":"Darnet: Bridging domain gaps in cross-domain few-shot segmentation with dynamic adaptation.arXiv preprint arXiv:2312.04813, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.674755Z"},"links":{"cited_paper":"/paper/2312.04813","citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:42a1d48f94b0316a02c951fd7a5397ea8de0d6577c8a880752218a90a961a26e","observation_id":"18ad9deb-59e3-4c70-a64c-99cb4795a9de","resolution":{"observed_at":"2026-08-03T04:23:19.674755Z","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":"2026-08-03T04:23:19.754746Z","title":"Self- support few-shot semantic segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.754746Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:962a21c66c331b6cb9b24c28c8e022b75d5d1ec7eac30bb7aac5903272a98363","observation_id":"068715f3-0e7b-403d-a876-1b10da9f570a","resolution":{"observed_at":"2026-08-03T04:23:19.754746Z","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":"2026-08-03T04:23:19.889649Z","title":"Adapt- ing in-domain few-shot segmentation to new domains with- out retraining.arXiv preprint arXiv:2504.21414, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.889649Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:8032a3973fc6c32f65d0834604bda4919a32477a8a4b28d2578d501379c71194","observation_id":"4e794a14-7653-496a-b0ee-e422bd9bc4ec","resolution":{"observed_at":"2026-08-03T04:23:19.889649Z","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":"2026-08-03T04:23:19.994823Z","title":"Cross-domain few-shot object detection via enhanced open-set object detector","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:19.994823Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:b96e24415632eafb2bad4573b4e45ea2a59b8b3273ac9ebe3d34b626817083bd","observation_id":"38524a05-8c23-469f-a425-94239054d674","resolution":{"observed_at":"2026-08-03T04:23:19.994823Z","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":"2026-08-03T04:23:20.116208Z","title":"Acrofod: An adaptive method for cross-domain few-shot object detection","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:20.116208Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:ab64d25319ffb5f6f5f315873cdde35d8ccd75765c81c0be0050dc1d47f42331","observation_id":"8d763a94-1c1e-4737-a45b-8ca4503afc6c","resolution":{"observed_at":"2026-08-03T04:23:20.116208Z","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":"2026-08-03T04:23:20.216001Z","title":"Simple copy-paste is a strong data augmentation method for instance segmentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:20.216001Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:88195871945a59b451bbec551d57c9561d5b59e6f11b9357c234a8d2f7114bc5","observation_id":"38a9e6b3-11d2-40c2-8ebf-32dba059d080","resolution":{"observed_at":"2026-08-03T04:23:20.216001Z","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":"2026-08-03T04:23:20.400482Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:20.400482Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:be67c923f7943b307132fac133890a1c4d49a717e10a205074358884dad54770","observation_id":"b59bf0b8-a630-45c8-a49c-a892e26df37f","resolution":{"observed_at":"2026-08-03T04:23:20.400482Z","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":"2026-08-03T04:23:20.554235Z","title":"Apseg: Auto-prompt network for cross-domain few-shot semantic segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:20.554235Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:5d3125cac5b71c006acb4c3c1cf918fbc32d55b9bf90cb7eb5b75580b8843f7a","observation_id":"2467e82b-46e2-46d6-986e-e3597069b973","resolution":{"observed_at":"2026-08-03T04:23:20.554235Z","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":"2026-08-03T04:23:20.705144Z","title":"Adapt before comparison: A new perspective on cross-domain few-shot segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:20.705144Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:b39e37793fae8fa4e6c10cf6e5ed1fa2a6218a4ed46635d562a77361bedcf77b","observation_id":"f73fc0a1-8a49-4456-b61f-ddb3692908be","resolution":{"observed_at":"2026-08-03T04:23:20.705144Z","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":"2026-08-03T04:23:20.825640Z","title":"Cross attention network for few-shot classi- fication","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:20.825640Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:f3cddf41471d9808beccea79ad9f6d5ba1847c156cfe645d4aa9f24ddb16a14e","observation_id":"89333235-e389-4169-9e89-4ede0511b05c","resolution":{"observed_at":"2026-08-03T04:23:20.825640Z","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":"2026-08-03T04:23:21.005014Z","title":"Restnet: Boosting cross-domain few-shot segmentation with residual transformation network","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:21.005014Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:a4e27e7a947bbc079edb0c991320b1da2d01ed6de654f16a9a35d5a8ba39c587","observation_id":"d8f522f9-750d-4c37-8a3e-14a6dd80222d","resolution":{"observed_at":"2026-08-03T04:23:21.005014Z","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":"2026-08-03T04:23:21.224426Z","title":"Semantic segmentation of underwater im- agery: Dataset and benchmark","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:21.224426Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:2fc8cd2946edd01678d80911d200a4269e4ae079d1cddc848d3128dfe4f565c5","observation_id":"684ac5ec-9301-4ecc-bc49-4ff32b1743db","resolution":{"observed_at":"2026-08-03T04:23:21.224426Z","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":"2026-08-03T04:23:21.376544Z","title":"Automatic tuberculosis screening using chest radio- graphs.TMI, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:21.376544Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:ab02dd46495a066648b07f099889b6f2f77bb2e0f38346d11ce9eecdbadb7e26","observation_id":"065fb348-27c6-4996-bf59-9cca9217d1f2","resolution":{"observed_at":"2026-08-03T04:23:21.376544Z","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":"2026-08-03T04:23:21.441151Z","title":"Few-shot object detection via feature reweighting","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:21.441151Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:01fb3f8ce1fb2da10e8c90121057b9cee78c67ae827979adda7bdec87463d59e","observation_id":"38f0a9ab-fda9-4e96-9e74-9e0c16447ca6","resolution":{"observed_at":"2026-08-03T04:23:21.441151Z","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":"2026-08-03T04:23:21.519727Z","title":"Relational embedding for few-shot classification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:21.519727Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:97e68331b4882805ae4ff093dbd431f18f5304c6c4570b7ed5f429a671bf76a1","observation_id":"d417a86a-d44f-4f70-acbe-9c92594007dd","resolution":{"observed_at":"2026-08-03T04:23:21.519727Z","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":"2026-08-03T04:23:21.637779Z","title":"Segment any- thing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:21.637779Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:3d4eb5ed4d5c6a00ce954fc41d62868806c71c97dd826f2a1ff5492a17eb7c8e","observation_id":"a575693e-8b83-4385-b56c-58178c7e35a3","resolution":{"observed_at":"2026-08-03T04:23:21.637779Z","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":"2026-08-03T04:23:21.814052Z","title":"Learning what not to segment: A new perspective on few- shot segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:21.814052Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:8901ef8a204b0558511b2f81af8b6003c041819659db673178d4f67283f9348f","observation_id":"7bcdb409-26e4-4f38-96e4-8904649e2161","resolution":{"observed_at":"2026-08-03T04:23:21.814052Z","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":"2026-08-03T04:23:21.997807Z","title":"Base and meta: A new perspective on few-shot segmentation.TPAMI, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:21.997807Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:e94a44cf4653757b199d4778b464143063f6f93033ff9345affe83ca3e6ce574","observation_id":"c63d2c9a-7e41-45d6-b1c4-1f88c08fe11e","resolution":{"observed_at":"2026-08-03T04:23:21.997807Z","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":"2026-08-03T04:23:22.123354Z","title":"Cross-domain few-shot se- mantic segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:22.123354Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:43663d4e18a88a2455676196fff49d3d007dfd84ee42fe2733582941eeda0213","observation_id":"dd018d19-e8ee-41fa-87bc-0d57ae7abca2","resolution":{"observed_at":"2026-08-03T04:23:22.123354Z","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":"2026-08-03T04:23:22.243389Z","title":"Adaptive prototype learning and allocation for few-shot segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:22.243389Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:8a1456c78edfa02c473d1ad7be630ebedeef221e4bedea831663d09010a3e08f","observation_id":"ba57faaf-0614-45b1-a737-dec034792610","resolution":{"observed_at":"2026-08-03T04:23:22.243389Z","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":"2026-08-03T04:23:22.333620Z","title":"Fss-1000: A 1000-class dataset for few- shot segmentation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:22.333620Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:158cff31cc4f469a45019ef1d71b45a194e34570ee9ba9d8a8c1187dc2f92d95","observation_id":"20a58838-53d0-47ae-a71a-824789368824","resolution":{"observed_at":"2026-08-03T04:23:22.333620Z","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":"2026-08-03T04:23:22.468613Z","title":"Constructing self-motivated pyramid curriculums for cross- domain semantic segmentation: A non-adversarial approach","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:22.468613Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:d27354c5fe568a57ca3d3da5dc66952b4b385ce822fa77a41acca04f26e07700","observation_id":"9ac1c39e-3c40-4bf6-983a-ae05e60756b0","resolution":{"observed_at":"2026-08-03T04:23:22.468613Z","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":"2026-08-03T04:23:22.599067Z","title":"Inter- mediate prototype mining transformer for few-shot semantic segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:22.599067Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:8c113216d10302fccf2cbb127c6b0d01723ce74928e8629b6d6f85ea8ebda57e","observation_id":"f6172395-ec17-4326-958c-f2076b116c17","resolution":{"observed_at":"2026-08-03T04:23:22.599067Z","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":"2026-08-03T04:23:22.726827Z","title":"Simpler is better: Few-shot semantic segmenta- tion with classifier weight transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:22.726827Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:9f20b5222951f436f17d64333e3ed8fee710fc2103edb9468dcd28983a67ed07","observation_id":"5d7112ab-6ae1-457c-b859-a5a66fe0199f","resolution":{"observed_at":"2026-08-03T04:23:22.726827Z","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":"2026-08-03T04:23:22.877836Z","title":"Pfenet++: Boosting few-shot semantic segmentation with the noise-filtered context-aware prior mask.TPAMI, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:22.877836Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:34a00e654c9a727a6d93d5fb8237ea02b1db9eb856e1e472b465937de57828b5","observation_id":"c1f0baee-c4fb-4411-900a-7e7c2aa90f7e","resolution":{"observed_at":"2026-08-03T04:23:22.877836Z","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":"2026-08-03T04:23:23.028161Z","title":"Hypercorrela- tion squeeze for few-shot segmentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:23.028161Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:47648ec62ee23da615b4229f79489d890fb511be4c6a4503f217bd4d40c928ef","observation_id":"6410397b-0b49-4236-9dc3-bcbfdd077808","resolution":{"observed_at":"2026-08-03T04:23:23.028161Z","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":"2026-08-03T04:23:23.182544Z","title":"Msi: Maximize support-set information for few-shot segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:23.182544Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:8eb5a001709c31563d775f598abda218e5a3f04a3685622572935820f91e3b5c","observation_id":"8d702527-1b6c-44d2-a35a-56cd49148e40","resolution":{"observed_at":"2026-08-03T04:23:23.182544Z","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":"2026-08-03T04:23:23.315474Z","title":"Cross-domain few-shot segmentation via iterative support-query correspon- dence mining","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:23.315474Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:6ab50b310cef2f99451fced329e8c278e9039f7e524ddf3125ea3e58421e0e30","observation_id":"9922353f-714a-4e05-9aaa-3a475258f38b","resolution":{"observed_at":"2026-08-03T04:23:23.315474Z","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":"2026-08-03T04:23:23.473984Z","title":"Automatic differentiation in pytorch","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:23.473984Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:9ed57f29786aacbf20f7828fdc4cb9ea16fbfcb03cf4c78605d230820e2be53c","observation_id":"777f8443-b80e-4253-9dfe-0c30f823736c","resolution":{"observed_at":"2026-08-03T04:23:23.473984Z","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":"2026-08-03T04:23:23.599483Z","title":"Hierarchical dense correlation distillation for few-shot segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:23.599483Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:3dbc3f4f30ba6357a67601d06677b07324e22de6468adc49ad3e07cf1b413cdc","observation_id":"943cea2d-aa7d-4fa1-8e05-5a5893797c2e","resolution":{"observed_at":"2026-08-03T04:23:23.599483Z","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":"2026-08-03T04:23:23.728516Z","title":"Sam-aware graph prompt reasoning network for cross-domain few-shot segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:23.728516Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:e014744b9371fe14eea46e57bdb2180568a4e03426ee2528e0fd45a1759c01f7","observation_id":"08c05f3a-1b28-4efe-8023-40b54a102811","resolution":{"observed_at":"2026-08-03T04:23:23.728516Z","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":"2026-08-03T04:23:23.851474Z","title":"Aligndiff: aligning diffusion models for general few-shot segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:23.851474Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:68772490f60dc51a5b92b1db44d5a4a5e9603aad53ef2abbd5ebaf0d24cf950c","observation_id":"e6849c6c-3bb6-4568-8c98-62102c30f56b","resolution":{"observed_at":"2026-08-03T04:23:23.851474Z","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":"2026-08-03T04:23:24.025234Z","title":"Guided curriculum model adaptation and uncertainty-aware evalua- tion for semantic nighttime image segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:24.025234Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:44714f8f09cc19f0600261043412abcde7e6de664161768a29322a1d66c063d2","observation_id":"90d7ebb8-308e-48ca-b882-4857d857e502","resolution":{"observed_at":"2026-08-03T04:23:24.025234Z","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":"2026-08-03T04:23:24.208055Z","title":"Cdfsl-v: Cross-domain few- shot learning for videos","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:24.208055Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:e82cef666926c7108e12ec709595767de0b73b44053fb7e149351ce315ec98b3","observation_id":"8d778396-d4ee-4e49-8d80-b24c2152ce28","resolution":{"observed_at":"2026-08-03T04:23:24.208055Z","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":"2026-08-03T04:23:24.408422Z","title":"One-shot learning for semantic segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:24.408422Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:50d2fc43b47011e9ee7e9292225ceec8004ea1c42c6278d408ffdc146aee50bb","observation_id":"4f96f154-da89-43b2-8c77-0b7f88e3e83b","resolution":{"observed_at":"2026-08-03T04:23:24.408422Z","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":"2026-08-03T04:23:24.577090Z","title":"Amp: Adaptive masked proxies for few-shot segmentation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:24.577090Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:5727bd3d1d5d701368929a933a8490225a595453756a47ae0ad9fc94ca7344fb","observation_id":"58199848-6d2d-460c-93b0-53b179dbdebf","resolution":{"observed_at":"2026-08-03T04:23:24.577090Z","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":"2026-08-03T04:23:24.660195Z","title":"Prototypical networks for few-shot learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:24.660195Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:170a480b2d227a3eab95f317ca3877576694cf4e7c8c43e171678093f6a5c4ee","observation_id":"a6e90322-436d-4ce0-acb6-51a2645a464a","resolution":{"observed_at":"2026-08-03T04:23:24.660195Z","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":"2026-08-03T04:23:24.742854Z","title":"Domain-rectifying adapter for cross-domain few-shot segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:24.742854Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:58022635bfef3df5f7dc06e4f0c8231b0a15913f51c34f2fc4896a0073a11aef","observation_id":"c32dd6c3-5017-45ac-90ba-e0b84c0f7595","resolution":{"observed_at":"2026-08-03T04:23:24.742854Z","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":"2026-08-03T04:23:24.806395Z","title":"Prior guided feature enrich- ment network for few-shot segmentation.TPAMI, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:24.806395Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:b1ae7b6acd960ddaf1175b284da6cad943277ebc4bc08716e44436139f7bc2d3","observation_id":"e7e55686-5675-469e-abaf-a88b6ff88129","resolution":{"observed_at":"2026-08-03T04:23:24.806395Z","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":"2026-08-03T04:23:24.973628Z","title":"Lightweight frequency masker for cross-domain few-shot se- mantic segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:24.973628Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:abba8b830b2d2c8c32f1ccca0accf736977378d86e4e2375c44d40f0e207b0c2","observation_id":"1afcfda8-a296-4592-bbb2-5d17669a1815","resolution":{"observed_at":"2026-08-03T04:23:24.973628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.02677","last_updated":"2025-06-03T09:23:20Z","snapshot_observed_at":"2026-08-08T12:35:39.079836Z","submitted_at":"2025-06-03T09:23:20Z","title":"Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.02677","snapshot_observed_at":"2026-08-03T04:23:25.106198Z","title":"Self-disentanglement and re-composition for cross-domain few-shot segmentation.arXiv preprint arXiv:2506.02677, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:25.106198Z"},"links":{"cited_paper":"/paper/2506.02677","citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:6b0aca507abd9610d2da9d3cacdf400e4129e1c85e1d1077b8f3868a0546372e","observation_id":"7847456d-3bbd-450f-9c9b-6fa6ebf57c22","resolution":{"observed_at":"2026-08-03T04:23:25.106198Z","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":"2026-08-03T04:23:25.328818Z","title":"The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions.Sci- entific Data, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:25.328818Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:b30196e248dca085d2841201963ebd5c9420da0e01f5fe0b01b34efe645c8dbe","observation_id":"231b2e0f-8a03-4f24-8180-f7235d73abc6","resolution":{"observed_at":"2026-08-03T04:23:25.328818Z","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":"2026-08-03T04:23:25.495745Z","title":"Panet: Few-shot image semantic segmenta- tion with prototype alignment","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:25.495745Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:ceffbd31f9c02ac59f1295e63cf3985339a1b6b390adf7dc34eb934dc3712d2f","observation_id":"953fe7b9-174a-4ec0-ae15-3dd15f0e0e6b","resolution":{"observed_at":"2026-08-03T04:23:25.495745Z","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":"2026-08-03T04:23:25.732771Z","title":"Remember the differ- ence: Cross-domain few-shot semantic segmentation via meta-memory transfer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:25.732771Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:09b31d4adc0fc24fefbb3a6260d8bd457dc88cee1ccaa7393c03fb4333500dea","observation_id":"bfba5b26-d316-470b-abd1-8d1567099cfb","resolution":{"observed_at":"2026-08-03T04:23:25.732771Z","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":"2026-08-03T04:23:25.949891Z","title":"A survey on curriculum learning.TPAMI, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:25.949891Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:3cae6466392c8d68530b13ee7c852e375626e8c5d9d0b24cbc4ed286151239fa","observation_id":"2f6714f3-f04b-45c1-b377-e643be471fb3","resolution":{"observed_at":"2026-08-03T04:23:25.949891Z","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":"2026-08-03T04:23:26.096575Z","title":"Adap- tive agent transformer for few-shot segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:26.096575Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:f5f109eb8e945f3dc7c4a6a7b1cec2bcadcddfd522ac3cfae1ea14c478cb7102","observation_id":"b29ff388-15d4-453e-95f8-d6b61048793a","resolution":{"observed_at":"2026-08-03T04:23:26.096575Z","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":"2026-08-03T04:23:26.292993Z","title":"Task-adaptive prompted transformer for cross-domain few-shot learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:26.292993Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:130b00f6aad08579cc2fea98821662f9ffd832b8db4ed5cb82b858f969d752d0","observation_id":"c6ab96c6-ea49-45bd-8acc-a21d3f0fabdd","resolution":{"observed_at":"2026-08-03T04:23:26.292993Z","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":"2026-08-03T04:23:26.440717Z","title":"Few-shot object detection and viewpoint estimation for objects in the wild.TPAMI, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:26.440717Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:4e8d5e8c455e5c0c05bf35849d0a55968398c4cc0ddc389f02d619a0ee79ce3d","observation_id":"1c00ee52-0147-4dde-91bb-25431f2a2352","resolution":{"observed_at":"2026-08-03T04:23:26.440717Z","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":"2026-08-03T04:23:26.576312Z","title":"Self-calibrated cross attention network for few-shot segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:26.576312Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:3457e6f452b4f5e705e5193378586ad0ffa1df215fdfcc7117fc0154005a5dcc","observation_id":"eef7e07d-559d-4fc6-b81b-fe88d80b9897","resolution":{"observed_at":"2026-08-03T04:23:26.576312Z","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":"2026-08-03T04:23:26.719054Z","title":"Eliminating feature ambi- guity for few-shot segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:26.719054Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:5e4b6d593a6e6f48f55a6025fcf99b7782f7816ce59566de47740ce72ae53544","observation_id":"84750dc9-802f-455b-b874-7ade1b3b08a3","resolution":{"observed_at":"2026-08-03T04:23:26.719054Z","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":"2026-08-03T04:23:26.859599Z","title":"Hybrid mamba for few-shot segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:26.859599Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:13ae03606aeb99f65d479273083c29b94c51fb7467906b17b62c13cb0d10abd5","observation_id":"5dd83b9c-0f83-42e8-9af5-abd65874736e","resolution":{"observed_at":"2026-08-03T04:23:26.859599Z","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":"2026-08-03T04:23:26.992882Z","title":"Prototype mixture models for few-shot semantic seg- mentation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:26.992882Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:8d4d229ce353a8a8bc6d8a7900d6f76a57de072e7a839fd79c34909e69264733","observation_id":"0a6356af-4909-46c9-b5b9-b11e2ab58d6d","resolution":{"observed_at":"2026-08-03T04:23:26.992882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.05393","last_updated":"2024-12-28T09:34:11Z","snapshot_observed_at":"2026-07-06T19:12:23.792177Z","submitted_at":"2024-09-09T07:43:58Z","title":"TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.05393","snapshot_observed_at":"2026-08-03T04:23:27.137388Z","title":"Tavp: Task-adaptive visual prompt for cross-domain few-shot segmentation.arXiv preprint arXiv:2409.05393,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:27.137388Z"},"links":{"cited_paper":"/paper/2409.05393","citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:e7c42402cfc36ece5177d1b3a25b70e8e6a39d5e83708948319e5b6c53fdb930","observation_id":"799dc44b-8d82-41c0-90b9-df4012f80d1c","resolution":{"observed_at":"2026-08-03T04:23:27.137388Z","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":"2026-08-03T04:23:27.259426Z","title":"Mi- anet: Aggregating unbiased instance and general information for few-shot semantic segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:27.259426Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:e8ed7c3754a276bf3251b3c5cb2b43ebf3c364d093f44b94d47c900234fdcda7","observation_id":"f04c8f28-a8a6-4559-9ea8-5af81a6961e3","resolution":{"observed_at":"2026-08-03T04:23:27.259426Z","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":"2026-08-03T04:23:27.421280Z","title":"Self-guided and cross-guided learning for few-shot segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:27.421280Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:7b5448a29ee8a7fb6e36b91c695435a1f766fd51b612afce4f85f09373929969","observation_id":"b1a2aaf8-452d-4211-a72a-bdde494a6fa8","resolution":{"observed_at":"2026-08-03T04:23:27.421280Z","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":"2026-08-03T04:23:27.522866Z","title":"Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:27.522866Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:c198c47395164715cc80a6459bd5c7bf0be428945be2cc7454487435f0f1f013","observation_id":"91525dfc-ff02-4362-943d-f7aae7647c6f","resolution":{"observed_at":"2026-08-03T04:23:27.522866Z","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":"2026-08-03T04:23:27.649657Z","title":"Canet: Class-agnostic segmentation networks with it- erative refinement and attentive few-shot learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:27.649657Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:a1939d3761e9cbc3c4e0bf90721bf7500f006f425441f97187f2ea9d336ea113","observation_id":"f3c9bcb1-0d32-44d2-bbd6-603d5c89f458","resolution":{"observed_at":"2026-08-03T04:23:27.649657Z","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":"2026-08-03T04:23:27.875444Z","title":"Few-shot segmentation via cycle-consistent trans- former","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:27.875444Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:dd1fb6bd6095046020cc392b501796d66048aaee5f3355e3be0f08408a429877","observation_id":"78b421c5-290c-4611-85f4-a9a004d8366b","resolution":{"observed_at":"2026-08-03T04:23:27.875444Z","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":"2026-08-03T04:23:28.055602Z","title":"Meta-detr: Image-level few-shot detection with inter-class correlation exploitation.TPAMI, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:28.055602Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:9852d55fd6260f157102b73b49814b72b7f85d0fe19df8d6eef84e54554fd3cb","observation_id":"ec3ce1cd-8907-4fff-9df4-92702d91a5c2","resolution":{"observed_at":"2026-08-03T04:23:28.055602Z","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":"2026-08-03T04:23:28.320344Z","title":"Personalize segment anything model with one shot","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:28.320344Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:28ea5a44a40f1c1964d358b67761bc151f72247e247d90e39f66a720f2439d1d","observation_id":"5f2d4518-9058-4499-bce9-ec739d6380f5","resolution":{"observed_at":"2026-08-03T04:23:28.320344Z","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":"2026-08-03T04:23:28.521654Z","title":"A curriculum domain adaptation approach to the se- mantic segmentation of urban scenes.TPAMI, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:28.521654Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:04eed507c7f9774475c774b0977e6f641befda361dd5e09e3e344f36600901c3","observation_id":"a2010f3b-c570-44c6-a3fc-8a689ace45a1","resolution":{"observed_at":"2026-08-03T04:23:28.521654Z","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":"2026-08-03T04:23:28.681011Z","title":"Addressing background context bias in few-shot segmentation through iterative modulation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:28.681011Z"},"links":{"citing_paper":"/paper/2602.05217"},"observation_digest":"sha256:bfc9e9acacfab88b8fdd8efd390deec1b9d2d4b9dd86578df0b83bd8e385062d","observation_id":"a656123f-ab1e-4117-90ed-147bcb5c6aa6","resolution":{"observed_at":"2026-08-03T04:23:28.681011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.05217","last_updated":"2026-05-31T10:23:37Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T00:40:46.996047Z","submitted_at":"2026-02-05T02:16:44Z","title":"Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":73,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":73},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2602.05217."}