{"as_of":"2026-08-08T23:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:42be69fb81bf7f179a1f71896c8707b3cd08e86046f9bec9157f591c003fae78","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:26:04.651499Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T13:11:04.391406Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-07T13:26:04.651499Z","title":"A normalized gaussian wasserstein distance for tiny object detection","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21868","last_updated":"2025-05-28T01:33:23Z","snapshot_observed_at":"2026-08-07T13:18:22.248356Z","submitted_at":"2025-05-28T01:33:23Z","title":"Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:04.651499Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2505.21868"},"observation_digest":"sha256:666ce6e334e5b722b3e986ac0dfdfb10927565ae91ef2e896a45cd53031e3afd","observation_id":"70dcdd5b-4b2e-490a-8489-7df632edcbd4","resolution":{"observed_at":"2026-08-07T13:26:04.651499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-07T04:44:10.048804Z","title":"A normalized gaus- sian wasserstein distance for tiny object detection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.09897","last_updated":"2025-06-11T16:13:38Z","snapshot_observed_at":"2026-08-08T09:29:44.481424Z","submitted_at":"2025-06-11T16:13:38Z","title":"CEM-FBGTinyDet: Context-Enhanced Foreground Balance with Gradient Tuning for tiny Objects","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T04:44:10.048804Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2506.09897"},"observation_digest":"sha256:64d48bc2f92b59b6b56925670d946ecdf48406329f6e879f4140ddf056cb86e9","observation_id":"a2f033dd-fc34-4cca-b78d-2a0c5b01c7be","resolution":{"observed_at":"2026-08-07T04:44:10.048804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-06T20:20:36.200276Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection, 2022, [arXiv:cs.CV/2110.13389]","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13359","last_updated":"2025-07-04T04:56:25Z","snapshot_observed_at":"2026-08-06T20:11:26.320137Z","submitted_at":"2025-07-04T04:56:25Z","title":"Open-Vocabulary Object Detection in UAV Imagery: A Review and Future Perspectives","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-06T20:20:36.200276Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2507.13359"},"observation_digest":"sha256:b2c37dd64924bf88fe19a062d8dfa596869dbce0d8f736c4c63ddb0613d78d61","observation_id":"cc6c963e-f2de-42c2-9062-6df36fd66bcb","resolution":{"observed_at":"2026-08-06T20:20:36.200276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-06T15:54:10.926327Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14855","last_updated":"2025-07-20T07:53:04Z","snapshot_observed_at":"2026-08-08T10:54:40.815697Z","submitted_at":"2025-07-20T07:53:04Z","title":"An Uncertainty-aware DETR Enhancement Framework for Object Detection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T15:54:10.926327Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2507.14855"},"observation_digest":"sha256:d74d4e716a6823fe52d1b78b45a72fadbc8f1cc560db53777a4433b8f295867e","observation_id":"be3bf902-f0cf-4297-9197-d655bf0546b7","resolution":{"observed_at":"2026-08-06T15:54:10.926327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-05T18:39:20.868011Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.14343","last_updated":"2025-08-20T01:37:17Z","snapshot_observed_at":"2026-08-07T17:37:54.433387Z","submitted_at":"2025-08-20T01:37:17Z","title":"Inter-Class Relational Loss for Small Object Detection: A Case Study on License Plates","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-05T18:39:20.868011Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2508.14343"},"observation_digest":"sha256:9f8f630f36ca19c4b284877d214eb6f73f6f3a2facd321a20d08b68368cda669","observation_id":"318c95fa-ba88-4f2d-b6de-6d187509a07d","resolution":{"observed_at":"2026-08-05T18:39:20.868011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-03T18:49:33.070969Z","title":"A normalized gaussian wasserstein distance for tiny object detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.03522","last_updated":"2026-07-28T03:05:48Z","snapshot_observed_at":"2026-08-06T20:08:56.898272Z","submitted_at":"2025-12-03T07:28:01Z","title":"MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T18:49:33.070969Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2512.03522"},"observation_digest":"sha256:58326a11bc5e76f7ae60b9fa0f2e04760a77273d8c54006f69f0ba21b75d0678","observation_id":"42368e21-4bc9-4389-ab72-01f47c4802df","resolution":{"observed_at":"2026-08-03T18:49:33.070969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":"2110.13389","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A normal- ized Gaussian Wasserstein distance for tiny object detection","venue":null,"work_id":"993aaf61-a99a-4523-b307-3bb3935e31ee","year":2021},"citing_paper":{"arxiv_id":"2604.06332","last_updated":"2026-04-07T18:13:55Z","snapshot_observed_at":"2026-08-04T02:17:12.505784Z","submitted_at":"2026-04-07T18:13:55Z","title":"Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-10T18:46:48.550865Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2604.06332"},"observation_digest":"sha256:5d8912eb1978047858e2dedd72c401e825d455153d346224383fa864ab46dd7c","observation_id":"7acdb703-1c50-45b1-a1bd-b8798cbdb697","resolution":{"observed_at":"2026-05-10T23:55:52.638762Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":"2110.13389","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A normal- ized Gaussian Wasserstein distance for tiny object detection","venue":null,"work_id":"993aaf61-a99a-4523-b307-3bb3935e31ee","year":2021},"citing_paper":{"arxiv_id":"2604.19233","last_updated":"2026-04-21T08:36:51Z","snapshot_observed_at":"2026-08-02T13:31:25.260894Z","submitted_at":"2026-04-21T08:36:51Z","title":"Adaptive Slicing-Assisted Hyper Inference for Enhanced Small Object Detection in High-Resolution Imagery","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-10T02:15:57.389751Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2604.19233"},"observation_digest":"sha256:b86fe5683a34f33a79829be0d7a83d8a89a61dc849065012b6ede9d74c709df9","observation_id":"165478e6-562d-42e7-96eb-0333124f528f","resolution":{"observed_at":"2026-05-11T13:11:04.396862Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-07-11T15:22:48.211209Z","title":"A normalized Gaussian Wasserstein distance for tiny object detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.04675","last_updated":"2026-07-06T05:01:54Z","snapshot_observed_at":"2026-08-05T15:14:40.426276Z","submitted_at":"2026-07-06T05:01:54Z","title":"ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-11T15:22:48.211209Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2607.04675"},"observation_digest":"sha256:7afd03c1764a55cd85e01a605764fb0d6d00df138fb033f28fb00547aa97c084","observation_id":"b6668761-4d61-4dfa-9d2e-630b2f809ac3","resolution":{"observed_at":"2026-07-11T15:22:48.211209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-01T18:04:16.360822Z","title":"A normalized gaussian wasserstein distance for tiny object detection.arXiv preprint arXiv:2110.13389, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.17423","last_updated":"2026-07-19T22:04:33Z","snapshot_observed_at":"2026-08-05T19:15:04.907010Z","submitted_at":"2026-07-19T22:04:33Z","title":"TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T18:04:16.360822Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2607.17423"},"observation_digest":"sha256:001686ae61db2f58e978a09cc466862d0a6fc4547553ae1b1e7debe8b5bc60b8","observation_id":"22e2741a-b09a-42a9-bf61-b6707c2e31d2","resolution":{"observed_at":"2026-08-01T18:04:16.360822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-01T14:07:56.563589Z","title":"arXiv preprint arXiv:2110.13389 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18877","last_updated":"2026-07-21T09:06:48Z","snapshot_observed_at":"2026-08-06T10:05:10.598119Z","submitted_at":"2026-07-21T09:06:48Z","title":"Physics-Informed Super-Resolution of Atmospheric Data","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-01T14:07:56.563589Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2607.18877"},"observation_digest":"sha256:135875d2214507e0835ef7cd429f39e5c1bfbffb8675f950da39b7910ccf154d","observation_id":"7c72b7df-5f02-4ddb-a81b-898f40dae0a4","resolution":{"observed_at":"2026-08-01T14:07:56.563589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13389","snapshot_observed_at":"2026-08-01T11:23:59.923005Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.19907","last_updated":"2026-07-22T08:39:46Z","snapshot_observed_at":"2026-08-03T09:23:20.798536Z","submitted_at":"2026-07-22T08:39:46Z","title":"TargetFinder: Detecting Widgets from Pixels on Desktop Interfaces","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-01T11:23:59.923005Z"},"links":{"cited_paper":"/paper/2110.13389","citing_paper":"/paper/2607.19907"},"observation_digest":"sha256:ebdc55e22518bfe97e399965c17a52086bed473074624ac598b4fff1ba15cd54","observation_id":"62978469-e35e-46c2-875c-18e562a83d17","resolution":{"observed_at":"2026-08-01T11:23:59.923005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2110.13389/citation-record","integrity":"/paper/2110.13389/integrity","json":"/paper/2110.13389/citation-record.json","paper":"/paper/2110.13389"},"outbound":[],"paper":{"arxiv_id":"2110.13389","last_updated":"2022-06-14T12:58:44Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T12:01:47.870599Z","submitted_at":"2021-10-26T03:43:17Z","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2110.13389."}