{"as_of":"2026-08-14T02:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6c1bca04389e595755a98a8fd12eb7ec0f57eb0dc082b4148d4ba8492fa834b5","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:36:00.416501Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2411.16772/citation-record","integrity":"/paper/2411.16772/integrity","json":"/paper/2411.16772/citation-record.json","paper":"/paper/2411.16772"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.365555Z","title":"A broadband hyperspectral image sensor with high spatio-temporal reso- lution","venue":null,"work_id":"c36b61d9-08d2-4a65-9c90-5e2ce593075c","year":2024},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.017648Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:aecf5329bc0fe7b21b4a7c8770ec8390a15682ca333fb9fff437f1bdacf36cae","observation_id":"d9081ba0-d5a5-443c-a3f8-08f3154ec197","resolution":{"observed_at":"2026-08-12T13:36:01.371574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.350518Z","title":"Mask-guided spectral-wise transformer for efficient hyper- spectral image reconstruction","venue":null,"work_id":"1d35dbd2-510d-4ea4-8dec-5c55df0c4d3b","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.030312Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:9f992288a74e1ca3be3a917b900f28d1546be8037fca608f39d6ffdf204a4e12","observation_id":"81e27e20-b722-4ce0-a306-21ecbc64a011","resolution":{"observed_at":"2026-08-12T13:36:01.356224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.338091Z","title":"Contrastive mean teacher for domain adaptive object detectors","venue":null,"work_id":"f52c0d78-e947-495a-9300-8f7491fac28a","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.038834Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:c2d88986186b21ff43e8619b0eff9b5d24bdb5349b9a85ec35ef63e58c43bed3","observation_id":"42e75c0a-5db1-4974-b5bb-6e0f0dfe7a52","resolution":{"observed_at":"2026-08-12T13:36:01.342328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.325935Z","title":"Constrained band selection for hyperspectral imagery","venue":null,"work_id":"930aa4eb-f508-40a9-87e4-392c39e685e6","year":2006},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.047548Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:96e6555f57728c60229d9b1f503bdec1ff7ea509ac5d2615b48da31c67f6f915","observation_id":"6b43933c-9d4d-4205-b52b-d81145051bd1","resolution":{"observed_at":"2026-08-12T13:36:01.330128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.310745Z","title":"Harmonizing transferability and discriminability for adapting object detectors","venue":null,"work_id":"23a21a10-6109-4e35-9853-a6d9102409b0","year":2020},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.056140Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:50e8b12c7744c60f136b53fb49e6a80f34ece75491d332806d4c9e5016ef3f9e","observation_id":"3812a1a8-bd2a-426c-b497-6d9311a5c412","resolution":{"observed_at":"2026-08-12T13:36:01.314912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.293328Z","title":"Learning domain adaptive object detection with probabilistic teacher","venue":null,"work_id":"f421b1f7-40b5-4fca-a678-92ceda643814","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.065103Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:cb0e5971437fbbe02a3091ea05d6aa193ab9bdec3305a3641ebdf54455b646e8","observation_id":"7f84b0c6-71af-452f-90ce-b51a9ff91de7","resolution":{"observed_at":"2026-08-12T13:36:01.300622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.264301Z","title":"Domain adaptive faster r-cnn for object de- tection in the wild","venue":null,"work_id":"8182619c-0ab7-438d-afc3-054d3d4c240e","year":2018},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.079821Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:56933da1f3207d70ca6871bfa0cfa9401f828e77108dc3533a73ccfb0966a18d","observation_id":"dc985c82-3e81-4cb1-9cb3-bf171f25b0ba","resolution":{"observed_at":"2026-08-12T13:36:01.271321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.246627Z","title":"Un- biased mean teacher for cross-domain object detection","venue":null,"work_id":"1535c586-1779-409e-926e-c0c6fcc84c3f","year":2021},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.088202Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:82ea441f67f32702ad81cbfa08153ab05271d61d9303c79c1d46799dca405d76","observation_id":"e6a5e863-0331-4d25-a50d-6d3229e94aee","resolution":{"observed_at":"2026-08-12T13:36:01.254299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.226037Z","title":"Harmo- nious teacher for cross-domain object detection","venue":null,"work_id":"86b118a7-4f83-4c15-8b9c-b0db04798d53","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.093881Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:7f6cee33be6224f0c4ac6d13f65d72564b91ff8b32e8b400b7677b7b3e2feed8","observation_id":"51d7ff22-a4aa-4a46-bdae-4475cfab6ee2","resolution":{"observed_at":"2026-08-12T13:36:01.231541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.206567Z","title":"D3t: Distinctive dual-domain teacher zigzagging across rgb- thermal gap for domain-adaptive object detection","venue":null,"work_id":"2bead67a-7a49-4f38-a77b-89d3113730ed","year":2024},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.106599Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:98682f19d336b162a87a96cb2813cc04627a8d6285bc2bb2e32aff43cfbd1a70","observation_id":"86f2aff1-3b38-4dbe-9d39-a8d4f1d55665","resolution":{"observed_at":"2026-08-12T13:36:01.213030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.188681Z","title":"Asymmetric weighted logistic metric learn- ing for hyperspectral target detection","venue":null,"work_id":"c0138726-3222-4270-bc31-8dc1cffc2674","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.110990Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:6b9a75c7a0875a740a4fd8157b794772b9cbf421261445670d662e8a34f612f3","observation_id":"b2bb9de2-627c-462a-a3e2-ebd6f82a1309","resolution":{"observed_at":"2026-08-12T13:36:01.193616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.172869Z","title":"Unsupervised domain adaptation by backpropagation","venue":null,"work_id":"837ebaa6-f6ff-4694-a102-fdc3e0f36a32","year":null},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.115513Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:f9ea6b788089a4b51350727a0c32695f58c5cd2b42727a914e2347fd0ee67bae","observation_id":"e24be91c-ee78-4948-bf60-27e5a24b9211","resolution":{"observed_at":"2026-08-12T13:36:01.178555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.157632Z","title":"Csda: Learning category-scale joint feature for domain adaptive object detection","venue":null,"work_id":"e83d8dd6-7262-44ee-ab23-e5dfed18a3d3","year":null},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.122711Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:7b04b890e76bdeaded211e639aa315d691d2d541f465e8a38a8d35a1ed8bca4c","observation_id":"e8076e52-079c-4371-9d81-3577b6830c7e","resolution":{"observed_at":"2026-08-12T13:36:01.162235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.142762Z","title":"Cross domain object detection by target-perceived dual branch distillation","venue":null,"work_id":"67fd69a3-ba63-4904-9fda-9da461be5d43","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.127797Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:9ef75e3385285a128302ebb36fcb8cb64d7f830a2f91814d6345bcf56cdd0ce4","observation_id":"d9a8818d-9e57-413a-8633-c175f0611f5a","resolution":{"observed_at":"2026-08-12T13:36:01.147602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.124930Z","title":"Surface plasmons-phonons for mid-infrared hyperspectral imaging","venue":null,"work_id":"b5397ed0-8395-4f81-8cab-34c7d1efd0c0","year":2024},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.132989Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:3acceda42eda15900b7d9f9e538d1df2fc62d801e5416d0fb9ed8beacd24bca0","observation_id":"d826d977-1488-4e6e-9f6a-81bd3b4233f1","resolution":{"observed_at":"2026-08-12T13:36:01.129629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.112191Z","title":"Every pixel matters: Center-aware feature alignment for domain adaptive object detector","venue":null,"work_id":"859247ff-9c03-447c-98db-e7d8f78f5a20","year":2020},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.140297Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:9418e7694b881adc95551ad059781de507036551704bc239f17562defb19ab98","observation_id":"64246fdd-7940-4d75-a988-ae91d6924d32","resolution":{"observed_at":"2026-08-12T13:36:01.116672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.099044Z","title":"Progressive domain adaptation for object detection","venue":null,"work_id":"c41bfcf7-e434-4fe5-af6f-5a4237524897","year":2020},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.145378Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:9cca6363fe3380294a0eb2c2b1ea3a120bf19f72409048ca15daec3b7387e3ed","observation_id":"d97ad914-9222-4e4b-8c7d-4b585deb91b8","resolution":{"observed_at":"2026-08-12T13:36:01.103447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.086574Z","title":"M2sodai: Multi- modal maritime object detection dataset with rgb and hyper- spectral image sensors","venue":null,"work_id":"5a18f0b9-4adb-4c64-92da-aab0fa75d894","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.149567Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:9f6c0bb9f2fbd941fc91b912e78334067bd571e76e1da805493192e6527b6b5b","observation_id":"81ddaaa0-6196-492a-bbb9-bea73a835843","resolution":{"observed_at":"2026-08-12T13:36:01.091623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.071446Z","title":"Decoupled adaptation for cross-domain object detec- tion","venue":null,"work_id":"a82f3035-5445-47b7-b435-5cfb6741917a","year":2021},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.154148Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:99c94e64d23d7d1e56b31fa54b5588a7ef472400ee1373cd1be5df0f9b5aebc2","observation_id":"adb4dcc6-1244-408e-95ba-6954ca9544de","resolution":{"observed_at":"2026-08-12T13:36:01.076202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.056844Z","title":"1 sparsity- regularized attention multiple-instance network for hyper- spectral target detection","venue":null,"work_id":"cc236cb5-efe8-42fc-9b75-580a343ec698","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.159361Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:5461cd800a841b4b62ee37f9d32574ffc8d5319e4a14ebd39f7de892bd76ff82","observation_id":"ba176ce8-bba4-471c-bde2-ed309f1619d9","resolution":{"observed_at":"2026-08-12T13:36:01.062096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.041779Z","title":"Hyperspectral anomaly detec- tion with attribute and edge-preserving filters.TGRS, 55(10): 5600–5611, 2017","venue":null,"work_id":"456dd338-08c4-4f75-a28f-ca8630ac726d","year":2017},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.172102Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:438e128b9e9603328bb393080db662f63e154503c66b3abf1bb6a1f1da7b951b","observation_id":"41566e14-26ba-4120-a1ed-98936612d1ad","resolution":{"observed_at":"2026-08-12T13:36:01.047142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.026124Z","title":"2pcnet: Two-phase consistency training for day-to-night unsupervised domain adaptive object detec- tion","venue":null,"work_id":"0be821aa-99dd-42ce-8236-79b4ac8ba67b","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.176990Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:fd6492b6ecc07a0942ab318dabd2142f6578a25cca77fa26743594421c01e7f9","observation_id":"2f59217c-5b9b-4fcd-99a2-62950fba4749","resolution":{"observed_at":"2026-08-12T13:36:01.032132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:01.008990Z","title":"Cat: Exploiting inter-class dynamics for domain adaptive object detection","venue":null,"work_id":"defdac64-8c58-459a-994f-8bc7483776e0","year":2024},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.187016Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:c38435114e8b8911138c009386b7ecd88da14b4db231d4403eaa7e905d85fd88","observation_id":"15be5a0b-0f4c-4e11-9f33-8705addf76d5","resolution":{"observed_at":"2026-08-12T13:36:01.014991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.991478Z","title":"Cross-domain object detection for autonomous driving: A stepwise domain adaptative yolo approach","venue":null,"work_id":"b39011c1-58cb-4501-b027-f5fe63357902","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.198824Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:ab19469e1bb0a60bff9c064a71f602a4ec2ff2d5aaf82d7ae108b035c4c5dfd5","observation_id":"d508685a-3149-4e2c-8ec5-2d0b38851e38","resolution":{"observed_at":"2026-08-12T13:36:00.998106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.977723Z","title":"Quantization-aware deep optics for diffractive snapshot hyperspectral imaging","venue":null,"work_id":"a7b59f8d-409b-4a10-a885-f159de2c02d1","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.207383Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:fff13e6318a18aecc8780a4cd25913230e7ad9c52b91c530f1d31424694f49dd","observation_id":"fb388722-ae28-4c2f-a664-0060f50bbb48","resolution":{"observed_at":"2026-08-12T13:36:00.982619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.961885Z","title":"Spectral enhanced rectangle transformer for hyperspectral image denoising","venue":null,"work_id":"d47d5ed7-456b-4e21-83bf-80e21d8156e0","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.214642Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:845c059644d3486113bfa553d1961926ba017bf1d475dfc72766a052719d3fb4","observation_id":"bcdf8a0b-300a-49a7-81d9-e26e32d7c473","resolution":{"observed_at":"2026-08-12T13:36:00.967115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.945949Z","title":"Sigma: Semantic- complete graph matching for domain adaptive object detec- tion","venue":null,"work_id":"dfaa1d6e-6ee2-441d-9305-75109e77a89c","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.220443Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:3a33848aa0b8c2ac450b923c687984c11a97f6877a636768710bfc11a3329053","observation_id":"bbd391e6-ddda-4021-a330-265874527e0a","resolution":{"observed_at":"2026-08-12T13:36:00.951306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.932086Z","title":"Target detection with unconstrained lin- ear mixture model and hierarchical denoising autoencoder in hyperspectral imagery","venue":null,"work_id":"7f3c277a-af25-45ad-8942-c36dda88354d","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.229819Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:f230d586a43d0bc954e8a19d4d2c3acbd577a0a7e6e30b6fa4f570d93b4fce36","observation_id":"13434870-0bc2-4756-a792-caaa697f7b2b","resolution":{"observed_at":"2026-08-12T13:36:00.936848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.918768Z","title":"Cross-domain adaptive teacher for object detection","venue":null,"work_id":"24b2ef52-cd2b-492b-ab09-24742184a644","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.242485Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:4db15d967dfdbd2c345a4644704cb38c635a2b964e865ada6006acdea3c68014","observation_id":"a5ab9cf8-0fa0-4dc4-b2f4-f0d5f9aabf39","resolution":{"observed_at":"2026-08-12T13:36:00.922950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.904806Z","title":"Decom- pose to adapt: Cross-domain object detection via feature dis- entanglement","venue":null,"work_id":"b78095dc-c629-40cc-94ea-dc188eb4776e","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.251805Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:b0df91da4e999c7c491629d91b76bc727fe788f43840304bbbb049b7e38a2b4a","observation_id":"6e9c6505-df98-40d7-a02e-c784d3994b57","resolution":{"observed_at":"2026-08-12T13:36:00.909570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.891349Z","title":"Confmix: Unsupervised domain adaptation for ob- ject detection via confidence-based mixing","venue":null,"work_id":"824ec1b4-f0e8-466c-9b01-c7dfee7b7172","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.261808Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:50858841ee8869464d6862b80a09bd79d923db8b2d41483be14a6c1e8a5004a8","observation_id":"8fdf0209-0996-4aa9-b458-40bef4b162ab","resolution":{"observed_at":"2026-08-12T13:36:00.896575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.876762Z","title":"Active domain adaptation with false negative prediction for object detection","venue":null,"work_id":"c686a2ce-0fbf-456c-90ba-2debae2582b1","year":2024},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.267659Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:fb728f427e927a7242e05eb910916138155121e8ec860eba492115aa9239d436","observation_id":"a430bf3e-11e8-4467-a449-284b02666838","resolution":{"observed_at":"2026-08-12T13:36:00.881491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.863134Z","title":"HTD- TS3: Weakly supervised hyperspectral target detection based on transformer via spectral–spatial similarity","venue":null,"work_id":"3fd5c504-6f4c-48bb-96b6-7bceac9d8b5e","year":2024},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.272593Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:ac32c1134327e98bdfd3c8d6c9da2da14f2a7c84fcaee2d058d96da97e1863b0","observation_id":"fb83df50-398c-4ec4-acfd-a6ea1dc8d8a7","resolution":{"observed_at":"2026-08-12T13:36:00.868087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.848372Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","venue":null,"work_id":"4afd28b9-0c8a-4dcf-bdad-3f1d20066924","year":2016},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.277705Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:5e59170d2faab0e9b71a7cc4bbdfadfc57b519cbc35a30bd371e193f15bf2861","observation_id":"3baaf928-d50c-4e08-8801-571665548666","resolution":{"observed_at":"2026-08-12T13:36:00.853758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.829044Z","title":"Prior-based domain adaptive object detec- tion for hazy and rainy conditions","venue":null,"work_id":"684b9f9d-cd55-426f-aad3-f250d4762b58","year":null},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.283743Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:aac4df465240a5d20871c9c26b66fd969be37606999227fe84920cc3bfece2dc","observation_id":"a9179211-6840-40fc-9c9c-4e90646fcb25","resolution":{"observed_at":"2026-08-12T13:36:00.834467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.814481Z","title":"Adapting object detec- tors with conditional domain normalization","venue":null,"work_id":"0c028fd4-f93e-4df9-8c29-8aedab51c71a","year":2020},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.290587Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:087bcb3d13d538899d744015d075d9b4f84917930a748998c42947bd260df07b","observation_id":"0c396cb2-f0ee-41cc-885e-6ee23b801b2d","resolution":{"observed_at":"2026-08-12T13:36:00.819421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.793979Z","title":"Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection","venue":null,"work_id":"2c8d2fc6-fa4a-4c75-9919-44f4fcabc6dc","year":2021},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.299568Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:a59f4cb9f7967fd447d7f2109f4175bec2c9ba880cd4bfb665093cf272ddcd30","observation_id":"91b7c059-5868-481f-a56d-bf5976d3d10c","resolution":{"observed_at":"2026-08-12T13:36:00.800485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.775906Z","title":"Meta-uda: Unsupervised domain adap- tive thermal object detection using meta-learning","venue":null,"work_id":"c7bc54ef-0979-4903-903a-56bebe644e64","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.305950Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:fdfd36546eb5aa9a4602cd1e3bc7ed54ca8873a0b1b09ab3529878897a69065b","observation_id":"82b135e8-babf-476b-8c92-b6cb2381937e","resolution":{"observed_at":"2026-08-12T13:36:00.781223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.758480Z","title":"Towards on- line domain adaptive object detection","venue":null,"work_id":"93d349d7-84b0-46fd-8023-0a180b7a43fa","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.313786Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:97f01dd2a2ef716cb2e92f665e9a9ea3bf04f6787fd23eee2f392117efe35a97","observation_id":"d8b856fd-f7de-4d65-b236-ec64fa944b8b","resolution":{"observed_at":"2026-08-12T13:36:00.763984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.722093Z","title":"Generalized autoencoder: A neural network framework for dimensionality reduction","venue":null,"work_id":"7bc580af-1add-432b-892c-6cf8c577da6b","year":2014},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.321433Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:2ac09211b7f2dc0badd25fd0885974d4573cdc0fcb201820b66252540d075165","observation_id":"7bb4d18f-de01-413a-9735-2d7a2925a032","resolution":{"observed_at":"2026-08-12T13:36:00.738429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.702352Z","title":"In2set: Intra-inter similarity exploit- ing transformer for dual-camera compressive hyperspectral imaging","venue":null,"work_id":"5f3fb8cf-fe5d-4c10-92dc-1fdf90fa91de","year":2024},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.325796Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:a825e36a3bb277660b9cc130f4ad41bbd1b1da7971f40985f6016c4c012333c4","observation_id":"5fa8e8eb-60ef-450f-a2af-8ea72a18dad7","resolution":{"observed_at":"2026-08-12T13:36:00.709921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.684583Z","title":"Instance-invariant domain adaptive object detection via pro- gressive disentanglement","venue":null,"work_id":"b9771c60-5850-41d3-89dc-cbc982e985a4","year":2021},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.337375Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:07610589bb4c2bcf305f539f6f6059c97c70460f2fc9aede8fa785e73215f134","observation_id":"9c87b9f7-aa51-4b8d-8425-c96ad0037611","resolution":{"observed_at":"2026-08-12T13:36:00.690665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.668891Z","title":"Exploring categorical regularization for domain adap- tive object detection","venue":null,"work_id":"4cbb1bf8-5abb-47da-8e9a-e041221aa3d8","year":2020},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.342428Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:a35ab854eb120dd7f394e88ae6581d80712f58768565caf1aff6e293244f236d","observation_id":"b37ae8da-1ea6-468a-858c-9b712d3cd778","resolution":{"observed_at":"2026-08-12T13:36:00.673358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.654719Z","title":"Cross-domain detection via graph-induced prototype alignment","venue":null,"work_id":"5f356d57-d143-4db2-9b4e-63acf7f073ef","year":2020},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.347598Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:186bfe89fc31b7b7dae9a2ff5e901f0f92bc0b27eacd63a516aee1baa8238422","observation_id":"03531bb1-a515-4603-be59-4ab023b42fee","resolution":{"observed_at":"2026-08-12T13:36:00.659321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.639046Z","title":"Multi-view adversarial discriminator: Mine the non-causal factors for object detection in unseen domains","venue":null,"work_id":"a847923e-ac63-439c-8d2d-e6003e579f3b","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.354223Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:5d7cb308f3241f426352a5e45bd95f60542d7faf75b2855a74d32c303f185c62","observation_id":"0cc82a2c-1c4e-4c5b-83d5-d04722875b46","resolution":{"observed_at":"2026-08-12T13:36:00.644198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.621162Z","title":"Sc-uda: Style and content gaps aware unsupervised domain adaptation for object de- tection","venue":null,"work_id":"9c944ecc-b51a-4380-afba-d5b608be049b","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.362897Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:8456eb9713e973623ad4ee1dccdb584e43916e7677a318e0456eedb7d5252a51","observation_id":"4d24b880-145f-4898-89c3-9a13af1ed029","resolution":{"observed_at":"2026-08-12T13:36:00.625820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.603929Z","title":"Detectron2","venue":null,"work_id":"09522b26-9879-4548-9c92-b2d36164dfc8","year":2019},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.368411Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:7d6b6f051e75f5dfea854a788bd51ce00b917ffb3f77d18644fcd0740df6da94","observation_id":"d6f1f61c-c035-4b91-9787-cff41c146fb5","resolution":{"observed_at":"2026-08-12T13:36:00.609518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.581152Z","title":"Discrimina- tive multiple instance hyperspectral target characterization","venue":null,"work_id":"bf9c2eed-c183-4e21-ae41-4c6c4ef6217d","year":2017},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.379502Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:f92236a8fdcf68dcb44f9783c57693aae09fe573eccfa18f6f193940a8695500","observation_id":"d2d18f26-aca1-44e9-879a-680b9e83a695","resolution":{"observed_at":"2026-08-12T13:36:00.592430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.554116Z","title":"Quantum- inspired spectral-spatial pyramid network for hyperspectral image classification","venue":null,"work_id":"cc9c2c2a-3768-44bd-ad02-184f25541498","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.387522Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:ba652a02992a7cbcd3bcf343c0fa62118e527f77c91b7b8757510f1cc920819b","observation_id":"1d7b689d-1d07-4610-8c7e-aa733c2c193a","resolution":{"observed_at":"2026-08-12T13:36:00.560042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.523516Z","title":"Task-specific inconsistency alignment for domain adaptive object detection","venue":null,"work_id":"2bf5e400-f510-41d1-bf88-6bebd9e3cab6","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.398472Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:6fe9348866c78a461199e4ef5e1b721c6922038701c257ac0ded49c9a81e556a","observation_id":"4e095170-1c28-4121-b82e-9f3e45849079","resolution":{"observed_at":"2026-08-12T13:36:00.534232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.493830Z","title":"Masked retraining teacher- student framework for domain adaptive object detection","venue":null,"work_id":"6360a20a-808b-4f38-8208-7d45a72876fc","year":2023},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.407273Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:d704a99cab8f62c9c98ca7d3184aa20c4f106003cec4506f89d417ca6a2df84a","observation_id":"b8b3f80d-61f5-432a-9785-daa5c700f592","resolution":{"observed_at":"2026-08-12T13:36:00.505591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:36:00.462838Z","title":"Multi-granularity alignment domain adaptation for object detection","venue":null,"work_id":"6448b2d2-74bc-4c4e-8cd5-63fed573d88a","year":2022},"citing_paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T13:36:00.416501Z"},"links":{"citing_paper":"/paper/2411.16772"},"observation_digest":"sha256:cdc2b323b3560a091e5177d9fda88a100b2c1b97ff57719bd5e80a6af68cb147","observation_id":"77932a86-1661-4447-818c-aef071b923f0","resolution":{"observed_at":"2026-08-12T13:36:00.475566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.16772","last_updated":"2024-11-25T06:42:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T13:29:20.367243Z","submitted_at":"2024-11-25T06:42:06Z","title":"Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":52},"total_outbound_references":52},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2411.16772."}