{"as_of":"2026-08-10T14:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:70a9c3e78972a11c01c414eaf4ac278b84782f253a29da9a0dbdd101c6cdc525","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T03:29:34.999362Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2602.07955/citation-record","integrity":"/paper/2602.07955/integrity","json":"/paper/2602.07955/citation-record.json","paper":"/paper/2602.07955"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T03:29:32.616322Z","title":"Divide and grow: Capturing huge diversity in crowd images with incrementally growing cnn","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:32.616322Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:b597fffae21fecd5c298fc7e614328b07a98cd161cbf05920191c6a0c4648a3d","observation_id":"b79f74e4-63c0-4dc8-8162-692149663fcb","resolution":{"observed_at":"2026-08-03T03:29:32.616322Z","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":"2026-08-03T03:29:33.079235Z","title":"Semantic generative augmentations for few-shot counting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.079235Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:401637207b8f07a94e2558df445b12744557f8cefc392e4587597bd244203022","observation_id":"fa680799-9bdf-4468-af61-65e6700258da","resolution":{"observed_at":"2026-08-03T03:29:33.079235Z","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":"2026-08-03T03:29:33.142818Z","title":"Domain-general crowd counting in unseen scenarios","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.142818Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:5805aecdbc79db93c3cf783434ee63b3d7adf185ff08643d145a1211c6731295","observation_id":"e0cc679d-28da-48c2-ae2f-451049fe0375","resolution":{"observed_at":"2026-08-03T03:29:33.142818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.12783","last_updated":"2020-03-28T13:17:30Z","snapshot_observed_at":"2026-08-07T18:38:02.258407Z","submitted_at":"2020-03-28T13:17:30Z","title":"CNN-based Density Estimation and Crowd Counting: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.12783","snapshot_observed_at":"2026-08-03T03:29:33.223783Z","title":"Cnn-based density esti- mation and crowd counting: A survey.arXiv preprint arXiv:2003.12783,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.223783Z"},"links":{"cited_paper":"/paper/2003.12783","citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:52703f6477f5d2d3ee8efa5a26680bbe10212ac8dee7b567b7f0858cc867b404","observation_id":"92b76ca1-335f-4044-ab26-8cca7a0612a1","resolution":{"observed_at":"2026-08-03T03:29:33.223783Z","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":"2026-08-03T03:29:33.449089Z","title":"Kang and A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.449089Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:991026e8b29a353a8be96893eab0f906fe54eeeadb84327d1929a44d30550d73","observation_id":"d075c312-1b9f-440e-b06c-6f300ea45ada","resolution":{"observed_at":"2026-08-03T03:29:33.449089Z","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":"2026-08-03T03:29:33.565306Z","title":"Vlcounter: Text-aware visual repre- sentation for zero-shot object counting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.565306Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:24671041fb001f48ff5e138c1f8919c564c9070f725254502abf1478341f73c5","observation_id":"df83e6c2-256b-42ef-8459-9fe752f31c40","resolution":{"observed_at":"2026-08-03T03:29:33.565306Z","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":"2026-08-03T03:29:33.675827Z","title":"Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.675827Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:c411c196aac146a11a9a2be862407eb5c9a5c9f73a1423e9850f8cbfbe52ded4","observation_id":"80b4aa62-fca0-4eff-89b4-9b3cebd36a86","resolution":{"observed_at":"2026-08-03T03:29:33.675827Z","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":"2026-08-03T03:29:33.879105Z","title":"Crowdclip: Unsupervised crowd counting via vision-language model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.879105Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:dc5de5f508c84d63e30fd561c7b5699810ad56e2d84213f03236aaae930ed9a9","observation_id":"26fec5af-cef4-4265-958b-4b92a38e8245","resolution":{"observed_at":"2026-08-03T03:29:33.879105Z","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":"2026-08-03T03:29:33.951101Z","title":"Attentive crowd flow machines","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.951101Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:6ca403525297c8c55ba1210b075cc9f8eeeb287718334c01eab6d911fd267ffe","observation_id":"5ca1eb2e-69eb-4b72-82cf-0ae9c80aece6","resolution":{"observed_at":"2026-08-03T03:29:33.951101Z","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":"2026-08-03T03:29:34.033194Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.033194Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:91d5b4357aa2a1f2c6bbce70908c7690cb3d5ee63ed89893e963abba2a7147b0","observation_id":"23291cca-8a72-4d18-a92c-8cd678d3c79a","resolution":{"observed_at":"2026-08-03T03:29:34.033194Z","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":"2026-08-03T03:29:34.277197Z","title":"Teaching clip to count to ten","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.277197Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:489b9422ff778ffa3838a28ed2e32e4592104a6636c29a6261d3e369802f475f","observation_id":"095a570f-57bd-4900-9965-cad98048ae37","resolution":{"observed_at":"2026-08-03T03:29:34.277197Z","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":"2026-08-03T03:29:34.399894Z","title":"Switchable whitening for deep representation learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.399894Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:1303b950bb2fe5858291a6c3452dfe0f2400fe2b423dafef3ddfd412244222bb","observation_id":"5937e8cc-e19f-4772-a5c4-21ea40eb9650","resolution":{"observed_at":"2026-08-03T03:29:34.399894Z","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":"2026-08-03T03:29:34.525439Z","title":"Dave - a detect-and-verify paradigm for low-shot counting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.525439Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:ee95f1c0a6c6aec72d02741faaaff1046e93caded206564836c52a5603664bed","observation_id":"c4e20529-72ec-41f6-9f6f-73e85f1973cc","resolution":{"observed_at":"2026-08-03T03:29:34.525439Z","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":"2026-08-03T03:29:34.647637Z","title":"Gary Chan","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.647637Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:b512a7288c7d471fc62868ddbfd21167dfa4a41862c053bd80a14c2efee55b49","observation_id":"2444bf5d-b848-4a56-bdef-22a204671b58","resolution":{"observed_at":"2026-08-03T03:29:34.647637Z","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":"2026-08-03T03:29:34.741490Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.741490Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:9c6f127fb00af0a0b9ba31c0781165447174d9a4a544841e7a7e035643e6542e","observation_id":"27a62437-30c9-4fda-8607-2ad4c1f3948d","resolution":{"observed_at":"2026-08-03T03:29:34.741490Z","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":"2026-08-03T03:29:34.856534Z","title":"Learning to count everything","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.856534Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:60987ea4366a758b0abf2949041f353db45c7a90a9043411b872b9ade83be101","observation_id":"e73edc1e-8842-4be4-9fe1-eaca4c98d290","resolution":{"observed_at":"2026-08-03T03:29:34.856534Z","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":"2026-08-03T03:29:34.898863Z","title":"Optimization as a model for few-shot learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.898863Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:bb8e95f9f70aa17cb9b39c9375442ee28733e2fd95da55503d36c5ed6591e040","observation_id":"1c263896-d055-4651-8d85-da5b90ec5fa6","resolution":{"observed_at":"2026-08-03T03:29:34.898863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10300","last_updated":"2024-06-01T03:35:22Z","snapshot_observed_at":"2026-08-10T09:35:36.209692Z","submitted_at":"2024-05-16T17:54:15Z","title":"Grounding DINO 1.5: Advance the \"Edge\" of Open-Set Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10300","snapshot_observed_at":"2026-08-03T03:29:34.954443Z","title":"Grounding dino 1.5: Advance the” edge” of open-set object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.954443Z"},"links":{"cited_paper":"/paper/2405.10300","citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:b6fb9fd3022be6931b32e75f10bacf1ecc4cdeb5130b4a88c8b97b0159b29035","observation_id":"89137b69-c5fd-4105-81f0-6253332b41d9","resolution":{"observed_at":"2026-08-03T03:29:34.954443Z","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":"2026-08-03T03:29:34.958382Z","title":"A compre- hensive survey of few-shot learning: Evolution, applica- tions, challenges, and opportunities.ACM Computing Sur- veys, 55(13s):1–40,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.958382Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:9233644cfeb8278c0374d2ce177b676079efd19900b8b30fa7cd805aacc4fde7","observation_id":"bc208c6c-0352-4f0e-9842-7348e5e0448e","resolution":{"observed_at":"2026-08-03T03:29:34.958382Z","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":"2026-08-03T03:29:34.962059Z","title":"Language-guided zero-shot object counting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.962059Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:7120ddf1e05883c84b588adba4689d73702551674f9dd479ce034f39425e43d7","observation_id":"f7f26fdb-003d-49b2-9e1b-3d1e9e4abc70","resolution":{"observed_at":"2026-08-03T03:29:34.962059Z","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":"2026-08-03T03:29:34.965745Z","title":"Detection, tracking, and counting meets drones in crowds: A benchmark","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.965745Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:f0f4381d0a88c00d878dd99d9ead61ee1ab02ba70441e1d98ad73dfb14abbac3","observation_id":"060144e6-6038-44bf-830b-3be392495ecf","resolution":{"observed_at":"2026-08-03T03:29:34.965745Z","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":"2026-08-03T03:29:34.969371Z","title":"Learning spatial similarity distribution for few- shot object counting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.969371Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:a24454bcdbc203409351b54b541218fc9fb9357b46d2c6329c46ee3c901a8b53","observation_id":"71d67f1c-198c-4d71-878b-2c08395f8ca6","resolution":{"observed_at":"2026-08-03T03:29:34.969371Z","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":"2026-08-03T03:29:34.972987Z","title":"Prototype mixture models for few-shot semantic segmentation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.972987Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:c6093ce4d5186dfe315ef3516b8f13859068de142064574ade1148ec29cb6a83","observation_id":"e5583150-1d1e-433d-965e-1013d5096a9a","resolution":{"observed_at":"2026-08-03T03:29:34.972987Z","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":"2026-08-03T03:29:34.976736Z","title":"Detclipv3: Towards versatile generative open- vocabulary object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.976736Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:23c021afddfa3e71eeff70d3ba4b4245d15cb5404e7a74b115dd634fb70a7b42","observation_id":"661f7a83-8378-4fbc-a6f3-cd915af5f69a","resolution":{"observed_at":"2026-08-03T03:29:34.976736Z","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":"2026-08-03T03:29:34.980779Z","title":"Few-shot object counting with similarity-aware feature enhancement","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.980779Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:4aa2e50eeb7df38c0b89b105e3eddb3d0765c985617ced439522097456ebe1e2","observation_id":"99676cc9-8ede-4c7e-904c-de1910ad52ab","resolution":{"observed_at":"2026-08-03T03:29:34.980779Z","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":"2026-08-03T03:29:34.984381Z","title":"Ro- bust head-shoulder detection by pca-based multilevel hog- lbp detector for people counting","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.984381Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:0a820734a70b762fbd1d41b1329e3acf83399b00c1cca4f35c008f039eca4d2b","observation_id":"79710791-09e9-445d-a912-ca42acf450ce","resolution":{"observed_at":"2026-08-03T03:29:34.984381Z","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":"2026-08-03T03:29:34.999362Z","title":"Daot: Domain-agnostically aligned optimal transport for domain-adaptive crowd counting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.999362Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:ccc020a73f296f63b529423986e076f76ddaa51ef3d7d4be3a8275067d501c19","observation_id":"fcbcfe44-4d7a-46cf-b87a-5d786ccd9bb7","resolution":{"observed_at":"2026-08-03T03:29:34.999362Z","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":"2026-08-03T03:29:33.754311Z","title":"Learning to generalize: Meta- learning for domain generalization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.754311Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:ad76d98b86a0ba5179e5cc23bb4563a476d8131a4daf72387dd2e6617c04201e","observation_id":"3b0622f4-eb59-4aec-a1b8-b475d4fdee2f","resolution":{"observed_at":"2026-08-03T03:29:33.754311Z","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":"2026-08-03T03:29:34.988268Z","title":"Cross-scene crowd counting via deep convolutional neural networks","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.988268Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:14a6f6cd60f558dccdcf7d9dcaf49c14bafc519ec2360366d1367028e3d5106e","observation_id":"ea105312-e605-4d77-b01c-155649808d47","resolution":{"observed_at":"2026-08-03T03:29:34.988268Z","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":"2026-08-03T03:29:32.736276Z","title":"Multi-task semi-supervised crowd counting via global to local self-correction.Pattern Recognition, 140:109506,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:32.736276Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:38aec2186b59e769e714172d619fcfb3800136a56c5549599edbbb34736cd110","observation_id":"e928a5c4-b081-4dc6-ab33-f87dc304cc50","resolution":{"observed_at":"2026-08-03T03:29:32.736276Z","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":"2026-08-03T03:29:34.991966Z","title":"Single-image crowd counting via multi-column convolutional neural network","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.991966Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:3afbc93d8064c8d1cb479dd57f8754161c1b8a434d216bd5404ced25336b2ca4","observation_id":"cb24163b-1ad8-4e84-90c8-bdecb6333cfc","resolution":{"observed_at":"2026-08-03T03:29:34.991966Z","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":"2026-08-03T03:29:34.995830Z","title":"Canet: Class-agnostic seg- mentation networks with iterative refinement and atten- tive few-shot learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.995830Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:9a1a42a2e4b00b46b8ce16676ef6eba47e96256e0ee7f0675a4a79f109b0ba82","observation_id":"fcfd715a-cad5-4b5a-9ad9-8c7a030fe701","resolution":{"observed_at":"2026-08-03T03:29:34.995830Z","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":"2026-08-03T03:29:34.950624Z","title":"Few- shot scene adaptive crowd counting using meta-learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.950624Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:c6f9edf2da1c11bb026c1a642e5035c55e508c8da3eb094222fa4de435f5ea88","observation_id":"f3ea840a-57ba-4fc8-aba4-4096225aee9e","resolution":{"observed_at":"2026-08-03T03:29:34.950624Z","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":"2026-08-03T03:29:32.701045Z","title":"Ten years of pedes- trian detection, what have we learned? InEuropean Conference on Computer Vision, pages 613–627","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:32.701045Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:c2799e8e58b9e4ca84ba3a55f28918ba0bf0d33bde509e15c385965a84ed29c0","observation_id":"025068b9-17ba-4b45-bcfe-3726751935a2","resolution":{"observed_at":"2026-08-03T03:29:32.701045Z","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":"2026-08-03T03:29:34.089380Z","title":"Dynamic prototype convolution network for few-shot semantic seg- mentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.089380Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:f009195d3f90d106ab870197b9347b43ebe1131a61481b78c3d1ff47848440fe","observation_id":"cef6dd19-81cf-4d54-aa8c-e62fceb9ac74","resolution":{"observed_at":"2026-08-03T03:29:34.089380Z","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":"2026-08-03T03:29:33.302788Z","title":"A survey of deep learning methods for density estimation and crowd counting.Vici- nagearth, 2(1):1–37,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.302788Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:849ebab8f9a10255c2a2df40ec0ed663d0339375cbd0632ba3541c57486881a5","observation_id":"c3ed6238-aa1b-422f-b98a-9153f19e05d8","resolution":{"observed_at":"2026-08-03T03:29:33.302788Z","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":"2026-08-03T03:29:33.033891Z","title":"Few-shot semantic segmentation with prototype learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:33.033891Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:287a488afc3e661cfb3129a0ec4e8e6e8bc7c84a385306590b37284d2c3f1e87","observation_id":"75920329-2087-49d7-9fc3-be4567aafa8c","resolution":{"observed_at":"2026-08-03T03:29:33.033891Z","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":"2026-08-03T03:29:34.174944Z","title":"Background noise filtering and distribution dividing for crowd counting.IEEE Transactions on Image Processing, 29:8199–8212,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:34.174944Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:6edc998b84f2ef5be5ed836b5d7a2c59040eede91e862e850904e74a5902a04c","observation_id":"3db29d3b-9924-42c7-9f49-781d2d0df24c","resolution":{"observed_at":"2026-08-03T03:29:34.174944Z","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":"2026-08-03T03:29:32.800070Z","title":"One- shot any-scene crowd counting with local-to-global guid- ance.IEEE Transactions on Image Processing, 33:6622– 6632,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:32.800070Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:16fb77baca2006226a69984c69e6e292c21ef3ce94a8fd33ab06f0826f05e577","observation_id":"34d49cfb-cba7-416e-b9c9-67e0a045fb7f","resolution":{"observed_at":"2026-08-03T03:29:32.800070Z","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":"2026-08-03T03:29:32.898493Z","title":"Crowd counting with crowd attention convolutional neural network.Neurocomputing, 382:210–220,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:32.898493Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:c44b0e9529dc837b9253def7b5029333233e276569a95e53894f0f51ace9399c","observation_id":"5399479d-6494-4aff-b05f-0f70008ead57","resolution":{"observed_at":"2026-08-03T03:29:32.898493Z","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":"2026-08-03T03:29:32.974610Z","title":"Robustnet: Improving domain generalization in urban- scene segmentation via instance selective whitening","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T03:29:32.974610Z"},"links":{"citing_paper":"/paper/2602.07955"},"observation_digest":"sha256:34a5a324872fee6bafbde9811733bdf0b8ac6479c450e27a5c373434c6459e0f","observation_id":"5233150e-96fb-4ce7-8424-75aaaba1fbd2","resolution":{"observed_at":"2026-08-03T03:29:32.974610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.07955","last_updated":"2026-05-30T07:52:24Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-03T03:29:31.961628Z","submitted_at":"2026-02-08T12:58:47Z","title":"One-Shot Crowd Counting With Density Guidance For Scene Adaptation"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":41,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":41},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2602.07955."}