{"as_of":"2026-08-07T19:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:14fbdc0175b95345a194800a636fe430ea70496cb8e04f426dab47b8dd2b1e8a","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:12:11.687547Z","state":"measured"},{"denominator":66,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":66,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T15:16:36.496400Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.19406","snapshot_observed_at":"2026-07-31T15:16:36.496400Z","title":"A global-local cross-attention network for ultra-high resolution remote sensing image semantic segmentation.arXiv preprint arXiv:2506.19406, 2025a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.24424","last_updated":"2026-07-27T13:36:08Z","snapshot_observed_at":"2026-08-03T02:41:13.403175Z","submitted_at":"2026-07-27T13:36:08Z","title":"MAViE: A Multi-scale Adaptive Vision Encoder for Fine-grained Visual Perception and Efficient Multimodal Reasoning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-31T15:16:36.496400Z"},"links":{"cited_paper":"/paper/2506.19406","citing_paper":"/paper/2607.24424"},"observation_digest":"sha256:93a27d8cc9bb6ea5400b9651f9be6026c8026ac6a7623c876c1d48ff7beddb12","observation_id":"aa5ec07b-db4a-4d1c-b66a-904a0e21d061","resolution":{"observed_at":"2026-07-31T15:16:36.496400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.19406/citation-record","integrity":"/paper/2506.19406/integrity","json":"/paper/2506.19406/citation-record.json","paper":"/paper/2506.19406"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:13.044218Z","title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","venue":null,"work_id":"78c9c789-a5e0-4cb6-8405-2480d67ace24","year":2017},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.757480Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:90d408584d74d4dc45d84fe1143f853bbd48118b059ab8f25d29707d035eed52","observation_id":"0f6bb193-9d88-4d21-bdf3-e842584a22e7","resolution":{"observed_at":"2026-08-06T23:12:13.047430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:13.035191Z","title":"Vision transformers for remote sensing image classification","venue":null,"work_id":"a962f957-4a54-40db-84db-96f5e6b94236","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.841543Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:34c26315a2896aa5418db39a8f063505469c4364f425d73d06e7aa5bac02e4f2","observation_id":"644f4750-abc1-43b9-b32a-db3e84e3ba79","resolution":{"observed_at":"2026-08-06T23:12:13.038302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:13.026237Z","title":"Sen1floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1","venue":null,"work_id":"1ab3d014-2d6f-4bba-b92c-713e3bc6f6b1","year":2020},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.986365Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:72490f2b7575ef745f6fb05f6fed066cb5ea74aaa52cc4e127f14b0324085759","observation_id":"8d8a4b2f-6311-44be-980f-66a7819c5e8f","resolution":{"observed_at":"2026-08-06T23:12:13.029416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:13.015701Z","title":"Crossvit: Cross-attention multi-scale vision transformer for image classification","venue":null,"work_id":"19f1a9ae-52f8-4688-8470-1965360f4cbe","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.108931Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:3b63145a947592206de572aba03d7dd3176ee45ecf7ea755fb5f6b22086f69c5","observation_id":"7ab01c28-633c-4cae-be17-078a117227da","resolution":{"observed_at":"2026-08-06T23:12:13.018744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:13.007168Z","title":"Remote sensing image change detection with transformers","venue":null,"work_id":"178f1367-d61c-4dc5-8ede-8f0303b3d078","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.220456Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:221e3484472b48f2ee3478f9beef5b455b79caf355f026186d24fcf0da73efae","observation_id":"12847b42-e71a-441c-a568-a521d043f5f7","resolution":{"observed_at":"2026-08-06T23:12:13.010026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07847","last_updated":"2025-06-09T15:09:49Z","snapshot_observed_at":"2026-08-07T05:21:44.165656Z","submitted_at":"2025-06-09T15:09:49Z","title":"F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07847","snapshot_observed_at":"2026-08-06T23:12:07.344003Z","title":"F2net: A frequency-fused network for ultra-high resolution remote sensing segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.344003Z"},"links":{"cited_paper":"/paper/2506.07847","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:63751e9fc25d46652ab5db7529212734fdd40191a15ab4395c193b8ddc52adee","observation_id":"749ffc13-91fa-4221-8250-cac0e2291c22","resolution":{"observed_at":"2026-08-06T23:12:07.344003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.7062","last_updated":"2016-06-07T04:00:08Z","snapshot_observed_at":"2026-07-06T04:04:27.398466Z","submitted_at":"2014-12-22T17:18:33Z","title":"Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.7062","snapshot_observed_at":"2026-08-06T23:12:07.469828Z","title":"Semantic image segmentation with deep convolutional nets and fully connected crfs","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.469828Z"},"links":{"cited_paper":"/paper/1412.7062","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:deda7963ded62794246613ec276e2b73d793e028232ef525313f426ca0e7802d","observation_id":"6451442e-0197-487e-a019-8440b8843107","resolution":{"observed_at":"2026-08-06T23:12:07.469828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.998311Z","title":"Deeplab: Semantic image segmentation with deep con- volutional nets, atrous convolution, and fully connected crfs","venue":null,"work_id":"f9500e22-d4d3-4acc-a429-b3eb8865dbab","year":2017},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.568008Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:22afcce700da57f2e37a4c1f2f4347b21fad5e1db93596b8526aeffc98c78266","observation_id":"45b20195-a5cf-44f3-9403-df0ee7f8b4c3","resolution":{"observed_at":"2026-08-06T23:12:13.001244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.988508Z","title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","venue":null,"work_id":"47036200-2582-4879-b019-e35d68df3653","year":2018},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.661533Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:fea33214b5b2e3b120349c210272d167881bdac90ad1e5ee7236210bcb4aea73","observation_id":"56034fb8-1ccb-4b67-af6f-71f95cc7bb16","resolution":{"observed_at":"2026-08-06T23:12:12.991881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.978936Z","title":"Lanet: Local attention embed- ding to improve the semantic segmentation of remote sensing images.IEEE Transactions on Geoscience and Remote Sensing , 59(1):426–435, 2020","venue":null,"work_id":"b0a9d4d5-9730-4ee7-8c33-c22489b4d2cc","year":2020},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.816412Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:3a226983f47ae1494136b4e7c5aca302d8c6f72bdfa02423942439f28927b5eb","observation_id":"e2ce1d46-6d23-46b4-b36e-666a4d2fa009","resolution":{"observed_at":"2026-08-06T23:12:12.982095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.969319Z","title":"Adversarial shape learning for building extraction in vhr remote sensing images","venue":null,"work_id":"c70d69b4-7a76-4c3b-b8bb-827dec5f2173","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.897216Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:0d61158b128d3bbba273bf4c70b87ccac140e10c00ff69a7a0a3d82748bbb3c8","observation_id":"2dd249b7-007a-4238-ac5c-8e3ad020e287","resolution":{"observed_at":"2026-08-06T23:12:12.972723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.960308Z","title":"Road ex- traction based on direction consistency segmentation","venue":null,"work_id":"bd324b90-8947-4fd5-b706-914eac66eeef","year":2016},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.925724Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:7d20ae1283514ad130eb92c9aa594c2edc9ebef75027c4c3d98df6f5fe4d4ae7","observation_id":"f51fcdbf-8ba4-482c-98b1-b7f1c44c5de7","resolution":{"observed_at":"2026-08-06T23:12:12.963486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17776","last_updated":"2024-05-28T03:12:33Z","snapshot_observed_at":"2026-07-06T18:21:01.419111Z","submitted_at":"2024-05-28T03:12:33Z","title":"The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17776","snapshot_observed_at":"2026-08-06T23:12:07.961317Z","title":"The binary quantized neural network for dense prediction via spe- cially designed upsampling and attention.arXiv preprint arXiv:2405.17776, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.961317Z"},"links":{"cited_paper":"/paper/2405.17776","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:3fcc9ffe53acf6cb553980c4a4620109e7371554e1ea2a253003955dd049e046","observation_id":"94863c14-cf0a-4b9a-8f2e-94f25b6e67ed","resolution":{"observed_at":"2026-08-06T23:12:07.961317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-06T23:12:08.063800Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.063800Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:f7d5faf77eaab410631120e5d3a8fd6711ac41966bdc32eb3e10bf2d1115b3ba","observation_id":"29f6c17d-7309-477e-9d7e-bd68833b967a","resolution":{"observed_at":"2026-08-06T23:12:08.063800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.950805Z","title":"Transform dual-branch attention net: Efficient semantic seg- mentation of ultra-high-resolution remote sensing images","venue":null,"work_id":"a20cdd67-93eb-4c7f-a54d-2fc04a78d7a0","year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.114946Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:40dce8dc56064d0dc7d86261ff840d079a7415dfaa61dafd7a540900be00c7a3","observation_id":"07a6507e-0610-41dd-94f9-2a24138ab963","resolution":{"observed_at":"2026-08-06T23:12:12.954012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.941151Z","title":"Multiscale refinement network for water- body segmentation in high-resolution satellite imagery","venue":null,"work_id":"afa58d84-c0bf-4eb6-9259-f614f5f078fe","year":2019},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.217735Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:b9c68f499c9bd29210bbe52da83a957a3a76f89155bc36d1b984c9cc4f659b6e","observation_id":"2de826fc-65b7-40de-b518-85765d998b83","resolution":{"observed_at":"2026-08-06T23:12:12.944135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:08.274580Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.274580Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:074cc2173317158e9d9085224761a0336531672fe8b5bd18b6e9d6708b154967","observation_id":"fe65f3ab-b394-4fef-9cf7-1cecb8917bee","resolution":{"observed_at":"2026-08-06T23:12:08.274580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.926682Z","title":"Ldnet: Semantic segmentation of high- resolution images via learnable patch proposal and dynamic refinement","venue":null,"work_id":"316983a3-0908-4f0a-b8b1-feccbbf2a693","year":2024},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.313023Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:769a8ce85fdf132ed5c3aefb538d55bbf7e3889ed115c1ccdc13cfd62cd301ac","observation_id":"7466a349-f72b-4837-9458-d5547514f994","resolution":{"observed_at":"2026-08-06T23:12:12.929686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.918167Z","title":"A method for stochastic optimization","venue":null,"work_id":"9de8e21b-c2fb-4711-90b6-12ae79c57382","year":2015},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.393068Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:93fddc00fe524b90a8e8ea6b4595a228bad54bb1265f337e313e588ad7ed11c7","observation_id":"0a397536-a1ee-4bcf-9b94-8fbd4ad0e4d5","resolution":{"observed_at":"2026-08-06T23:12:12.920860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.908780Z","title":"Global-local attention network for semantic segmentation in aerial images","venue":null,"work_id":"1e53f2b4-ea1f-4297-8e48-267699579dfa","year":2020},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.444616Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:bdd641290eb534b396b0d10f3fa46d42f6a57598d7e16fafd65d921e42b2a38b","observation_id":"d4dc2130-b4fc-43e7-8da6-f4349dcda3ac","resolution":{"observed_at":"2026-08-06T23:12:12.912475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.900482Z","title":"Energy mini- mum regularization in continual learning","venue":null,"work_id":"12d3e192-23c9-43a8-99a3-7af0cdf76a90","year":2020},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.539676Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:f43f8465c51f2eb63bb89c75fcdeb716520fd6f5e8e952a08c3df88f5a873dc0","observation_id":"c74f33f5-9b79-4350-ab46-3c120b5acae4","resolution":{"observed_at":"2026-08-06T23:12:12.903355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.892441Z","title":"Fusing multitask mod- els by recursive least squares","venue":null,"work_id":"6bc3c63d-dae5-455a-9039-5bcd7e2bf0b8","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.595915Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:1da2a19d0377c97fa807efb677a96dc4d0ddde327a50d2fa94bfaaf3edfb9dd8","observation_id":"35aac60c-1e22-4287-ba68-669db94fccba","resolution":{"observed_at":"2026-08-06T23:12:12.895333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.884235Z","title":"Refinenet: Multi-path refinement networks for high-resolution semantic segmentation","venue":null,"work_id":"e8280db4-df1e-4d5e-acd6-231f9095a774","year":1925},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.651714Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:1c12786d1fdd2a613b1c0be54c352c6656e60f703b526e6f34ac2f7e8fc059f4","observation_id":"e505fb61-1769-4c38-8eeb-06e7a92d1a72","resolution":{"observed_at":"2026-08-06T23:12:12.887018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.875554Z","title":"Feature pyramid networks for object detection","venue":null,"work_id":"7c20c5e1-e485-47f2-8d37-ccd52c5f9cdc","year":2017},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.722314Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:dd224a136fe9a649c0a5201c282fda07392757cfdadf8861d4c6b9736ff046f9","observation_id":"f35508ee-e01e-48cd-87aa-b103c5745e70","resolution":{"observed_at":"2026-08-06T23:12:12.878761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.867252Z","title":"Focal loss for dense object detection","venue":null,"work_id":"4e762d34-ce30-4a25-982b-d9c73f6e7f95","year":2017},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.775273Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:b3b6038e6819e2a6a8e796ce65a80190885e70f3bca037766ce1451573fd3cd0","observation_id":"4d38c4b8-9eee-4a42-a3bc-8a08a446fcdd","resolution":{"observed_at":"2026-08-06T23:12:12.869951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.858697Z","title":"Connecting image denoising and high-level vision tasks via deep learning","venue":null,"work_id":"bc8e2c4f-1753-492b-b250-838090ee5261","year":2020},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.887905Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:538856da7a08cd7d6cdde2de52a256e9cb4f6de65a7c62858326b0723f73c79f","observation_id":"d607f649-2f3a-443f-8c07-40fa33cdd820","resolution":{"observed_at":"2026-08-06T23:12:12.861778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04284","last_updated":"2018-04-16T19:33:33Z","snapshot_observed_at":"2026-07-06T05:46:43.820045Z","submitted_at":"2017-06-14T00:04:56Z","title":"When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.04284","snapshot_observed_at":"2026-08-06T23:12:08.951527Z","title":"When image denoising meets high-level vision tasks: A deep learn- ing approach","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:08.951527Z"},"links":{"cited_paper":"/paper/1706.04284","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:9f507fa69e70545f89f17ed7372dc2c1c5d7683cb6f062eff263b8f16451b6cd","observation_id":"81bb0838-976b-4e11-9f9e-40acbcf020c5","resolution":{"observed_at":"2026-08-06T23:12:08.951527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.849293Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":"a8de04bf-f841-4a0b-8ecd-b28864030052","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.014302Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:6e4369a9db4da7350451dfacb2d08949da92d5df3f2ac508bd2b554c47a4b665","observation_id":"ef3b9aa2-b070-4993-bfa4-44fc95352b65","resolution":{"observed_at":"2026-08-06T23:12:12.852577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:09.060726Z","title":"Llm- cot enhanced graph neural recommendation with harmonized group policy optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.060726Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:71bba3591bca2bab607133acd5ba019ede740754ada23716ab87539a735aa497","observation_id":"f0fc7304-b806-49d7-94b8-601883141367","resolution":{"observed_at":"2026-08-06T23:12:09.060726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:09.119094Z","title":"Geogrambench: Benchmarking the geometric program reasoning in modern llms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.119094Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:447a41551c6a4efee7eb0f8a8ed93833b86ef66fd2d7b24f34496109269c02a7","observation_id":"31ef5e2f-3d11-43b7-811f-30e48b84c5f9","resolution":{"observed_at":"2026-08-06T23:12:09.119094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.839733Z","title":"Dlnet: A dual-level network with self-and cross-attention for high-resolution remote sensing segmentation","venue":null,"work_id":"bc040937-5671-4b8b-bcb1-60be33ac2c4b","year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.202594Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:0f8161759760d62539ad5cb8dd4bf0e9b0e2cd5ae630f0549f6315f4b76f0ebd","observation_id":"2e9faa40-4226-4960-9104-679356dcad01","resolution":{"observed_at":"2026-08-06T23:12:12.842960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.830204Z","title":"Learning decon- volution network for semantic segmentation","venue":null,"work_id":"18afd5c2-73ff-45d7-ad3c-c4ee4cb5025d","year":2015},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.245187Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:5b23c398bfea8e0ef838dc9500a8c5a49929b04ff31549c1e996a3f45c12211e","observation_id":"c944849e-99d5-4bf7-94cd-f54ebee84a00","resolution":{"observed_at":"2026-08-06T23:12:12.833742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.02147","last_updated":"2016-06-07T14:09:27Z","snapshot_observed_at":"2026-08-01T15:22:37.552585Z","submitted_at":"2016-06-07T14:09:27Z","title":"ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.02147","snapshot_observed_at":"2026-08-06T23:12:09.311541Z","title":"Enet: A deep neural network architecture for real-time semantic segmen- tation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.311541Z"},"links":{"cited_paper":"/paper/1606.02147","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:96d2e63800f88b021451564603f7023c2d715519065ac8f916d244453f3772a5","observation_id":"4948e8e4-e94f-457d-8b59-9c93e3fd6bb5","resolution":{"observed_at":"2026-08-06T23:12:09.311541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07209","last_updated":"2025-03-10T11:48:26Z","snapshot_observed_at":"2026-08-07T17:17:09.621194Z","submitted_at":"2025-03-10T11:48:26Z","title":"Synthetic Lung X-ray Generation through Cross-Attention and Affinity Transformation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.07209","snapshot_observed_at":"2026-08-06T23:12:09.364898Z","title":"Synthetic lung x-ray generation through cross-attention and affinity transformation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.364898Z"},"links":{"cited_paper":"/paper/2503.07209","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:035021077076d44e762dff334036989d12116287e13194a897ac2a233e757eaf","observation_id":"975487b3-6c79-448a-997e-a172274b03b1","resolution":{"observed_at":"2026-08-06T23:12:09.364898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.822125Z","title":"U-net: Convo- lutional networks for biomedical image segmentation","venue":null,"work_id":"b8bf40e2-2da5-4977-8d0f-d3d0ba0e51ba","year":2015},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.437289Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:57a8fb04b6196a3c8a714c3c8dc034e6ebf105a0b8601c6c61ff22c9cee944b5","observation_id":"1741521f-1773-4d3c-bffc-756bc2f75429","resolution":{"observed_at":"2026-08-06T23:12:12.825203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.814095Z","title":"Incremental few shot se- mantic segmentation via class-agnostic mask proposal and language-driven classifier","venue":null,"work_id":"50079fb3-20ce-4923-a1d6-2dde6ae5f2d0","year":2023},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.523360Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:871e3969c0291cb5e60ac35d24a9e521f6dcd4b5127c8e0e0e0cb16c219a5fe2","observation_id":"2b37e454-10cc-4741-b179-338f0e2a5c31","resolution":{"observed_at":"2026-08-06T23:12:12.816805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.805969Z","title":"Uhrsnet: A semantic segmentation network specifically for ultra-high-resolution images","venue":null,"work_id":"4615c420-11fc-4ec3-88fd-8e506b4cea48","year":2020},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.584051Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:a1dfe0d81e24434b0a83ab2f30ae3357bc4f910d4d8c485b59944cf425735f1e","observation_id":"b38cdb55-9c0f-44eb-b2a4-23c6a17af5be","resolution":{"observed_at":"2026-08-06T23:12:12.808708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.797396Z","title":"Decouple the high-frequency and low-frequency information of images for semantic segmentation","venue":null,"work_id":"725b9ec5-4376-4268-8c10-4e45f2abd20f","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.679119Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:9e791e1b2048d7da4ac0bd6323b2468b5128a8fa47f1289169dea32fb5a84585","observation_id":"f986315a-d189-4bb1-9baf-7e71111eeef8","resolution":{"observed_at":"2026-08-06T23:12:12.800263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.02441","last_updated":"2025-04-24T01:47:25Z","snapshot_observed_at":"2026-08-07T16:12:02.898369Z","submitted_at":"2025-04-03T09:58:19Z","title":"Cognitive Memory in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.02441","snapshot_observed_at":"2026-08-06T23:12:09.719434Z","title":"Cognitive memory in large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.719434Z"},"links":{"cited_paper":"/paper/2504.02441","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:e538e1a81b25793f4e134ea7ded1e6ff67b511d917c9bad5e8a544ceb84b662d","observation_id":"f01bf8a7-5e39-4d5e-a5a2-1f947112b516","resolution":{"observed_at":"2026-08-06T23:12:09.719434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.789176Z","title":"Densenet-based land cover classification network with deep fusion","venue":null,"work_id":"e03d17da-8206-488d-b794-5be4ed005d36","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.796576Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:70cf8acc573ce5c137cea3a9e7b2a6aee60ad37c58bf1ae2c9389dabe7e86837","observation_id":"532ccc68-84a7-4089-ba22-727980dcbaaf","resolution":{"observed_at":"2026-08-06T23:12:12.792294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.781073Z","title":"Mbnet: A multi-resolution branch net- work for semantic segmentation of ultra-high resolution images","venue":null,"work_id":"7815db7a-4c5f-4e2d-9606-d73caceb125d","year":2022},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.867287Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:e62b7a9c06ba9dd3609207b9383a108a5b141d08468e3cbe7e4e435360c06d35","observation_id":"151b1f2d-1ae0-42d2-8b83-283abbe69a5d","resolution":{"observed_at":"2026-08-06T23:12:12.783791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.773255Z","title":"Class-incremental learning for semantic segmentation in aerial imagery via distillation in all aspects","venue":null,"work_id":"5a560c24-d167-4daf-99d9-ee9113a1aa46","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:09.949185Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:4123c214538292c939535deb9729333deca18d0ebe5b2fd3f04829e0881646a3","observation_id":"b96005fa-addd-42e8-8d8c-ad29cd558afc","resolution":{"observed_at":"2026-08-06T23:12:12.776071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.764879Z","title":"Class-incremental semantic segmentation of aerial images via pixel-level feature generation and task-wise distillation","venue":null,"work_id":"bc3c1ff4-8e35-4747-b624-7a0846650d61","year":2022},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.005463Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:c6ed5183d7ef6c42ccbee1fd6d8292d1d4ee81f7dca8cf7a86a89b0cdc6b02d6","observation_id":"8f1040fa-6a77-463a-8e8e-52be60f15a07","resolution":{"observed_at":"2026-08-06T23:12:12.768171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.755754Z","title":"Boosting semantic segmentation of aerial images via decoupled and multilevel compaction and dispersion","venue":null,"work_id":"c31f76d9-6324-4e7b-927b-16360af098ac","year":2023},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.034522Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:c7cddef5c48913edb60610e01f1eab3cc72b83577179613e7dc18036479419cd","observation_id":"bfc42096-8a0a-4204-8370-28109be7413a","resolution":{"observed_at":"2026-08-06T23:12:12.759135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18078","last_updated":"2024-05-28T11:39:36Z","snapshot_observed_at":"2026-08-06T16:02:37.332941Z","submitted_at":"2024-05-28T11:39:36Z","title":"Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18078","snapshot_observed_at":"2026-08-06T23:12:10.104363Z","title":"Edge-guided and class- balanced active learning for semantic segmentation of aerial images","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.104363Z"},"links":{"cited_paper":"/paper/2405.18078","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:e849818a07ee6ee4407a506b620bbeeb92bdd0e7ff7eacdfb7aa405d029f88c5","observation_id":"f6c6f9d7-edc0-4a0d-b90b-87b027528c45","resolution":{"observed_at":"2026-08-06T23:12:10.104363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.746968Z","title":"A data-related patch proposal for semantic segmentation of aerial images","venue":null,"work_id":"1d65d2f4-4667-4222-a076-c20a91a6fda0","year":2023},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.168012Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:541ed28457db8b81156b829ee3bdce187b832621b59a525f04d3614374e567b9","observation_id":"14f955a4-dfdd-474a-8c0e-5a5cfe531ba8","resolution":{"observed_at":"2026-08-06T23:12:12.750185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18663","last_updated":"2024-05-28T23:57:48Z","snapshot_observed_at":"2026-08-05T09:04:23.297133Z","submitted_at":"2024-05-28T23:57:48Z","title":"Lifelong Learning and Selective Forgetting via Contrastive Strategy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18663","snapshot_observed_at":"2026-08-06T23:12:10.242188Z","title":"Lifelong learning and selective forgetting via contrastive strategy","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.242188Z"},"links":{"cited_paper":"/paper/2405.18663","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:8c3c9981c4efcf9b11354cbb57aa47de1b427fe2e1a8049daa42fb20cd2ed997","observation_id":"92f1fc04-d9ad-49af-91da-fc945fb6eb0f","resolution":{"observed_at":"2026-08-06T23:12:10.242188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19568","last_updated":"2024-05-29T23:22:12Z","snapshot_observed_at":"2026-07-06T18:22:18.644161Z","submitted_at":"2024-05-29T23:22:12Z","title":"Organizing Background to Explore Latent Classes for Incremental Few-shot Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19568","snapshot_observed_at":"2026-08-06T23:12:10.323725Z","title":"Organizing back- ground to explore latent classes for incremental few-shot semantic segmen- tation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.323725Z"},"links":{"cited_paper":"/paper/2405.19568","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:84de18693c7d7377a37eb068a54bf54728f1ccbf9fe7e5ca10999f04f615e145","observation_id":"c517f99f-995f-4cbd-a918-191baac49765","resolution":{"observed_at":"2026-08-06T23:12:10.323725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13306","last_updated":"2025-05-19T16:25:55Z","snapshot_observed_at":"2026-08-07T15:43:04.901398Z","submitted_at":"2025-05-19T16:25:55Z","title":"GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13306","snapshot_observed_at":"2026-08-06T23:12:10.366641Z","title":"Gmm-based comprehensive feature extraction and relative distance preser- vation for few-shot cross-modal retrieval","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.366641Z"},"links":{"cited_paper":"/paper/2505.13306","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:703ed85eda64e3270fd5ed9447bcef1df4f35e9fdda7064105fe8c4e3081513e","observation_id":"89ae6db9-1892-4864-9f21-f3d9f5a24de5","resolution":{"observed_at":"2026-08-06T23:12:10.366641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.738890Z","title":"Max-deeplab: End-to-end panoptic segmentation with mask trans- formers","venue":null,"work_id":"158c2b96-60f4-447a-b4af-c9e6946b0c85","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.486788Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:08a6c69de712e9320c81ea0332780ef96a22a441c0b23e3c286698e3a35478f7","observation_id":"08efd36f-fdd5-429d-9e3c-542c9558f1c0","resolution":{"observed_at":"2026-08-06T23:12:12.741820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.731124Z","title":"Deep high-resolution representation learning for visual recognition","venue":null,"work_id":"de7b7887-68a8-444d-8784-e86a0690b77b","year":2020},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.496700Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:4a2cc31f98ac4b9360bd0c20ba8110e085647fbd0fa5e5d4046cbe847dd4c187","observation_id":"5a9c3554-057b-400d-88b3-8d892f011e03","resolution":{"observed_at":"2026-08-06T23:12:12.733874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.722518Z","title":"Detect globally, refine locally: A novel approach to saliency detection","venue":null,"work_id":"6b8b1b67-e3c1-45ab-934e-126c15da5373","year":2018},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.569742Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:0cddc80a56472d3e71056574cde72a786652d5784c67c3d148ef4de2d96f0c6b","observation_id":"2028b8e9-5802-4cef-95b7-8c686fa041e4","resolution":{"observed_at":"2026-08-06T23:12:12.725655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.713410Z","title":"Non- local neural networks","venue":null,"work_id":"207d5d0b-a4e1-40f3-bd89-0fc19d8fbb2e","year":2018},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.666153Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:bd951518ce148bca57d607c754523cdc52825b86ff842afe3b00aaf2bf220692","observation_id":"438a8432-45b0-4b39-bb28-488642f703b0","resolution":{"observed_at":"2026-08-06T23:12:12.716101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.704448Z","title":"Continual learning for image segmentation with dynamic query","venue":null,"work_id":"61043094-dba2-4ad9-8a2b-31f2fe04f276","year":2023},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.734125Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:224d70f549ff9a154f267bb06bda91187db690102caf16bfa5883faa2f916407","observation_id":"670f3fbb-fc7c-4ba1-b92d-3f89a9c5455b","resolution":{"observed_at":"2026-08-06T23:12:12.707385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.694838Z","title":"Segformer: Simple and efficient design for semantic segmentation with transformers","venue":null,"work_id":"8359d8b2-4461-464a-8307-55543b21471e","year":2021},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.784903Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:0689c7ca1b24121feece643c84b24902c5689fcd148e0783cd807c4084c3a89a","observation_id":"3cd3b27d-502a-4670-a4d9-a1a74f483eab","resolution":{"observed_at":"2026-08-06T23:12:12.698253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.01277","last_updated":"2025-06-02T03:16:19Z","snapshot_observed_at":"2026-08-07T11:42:57.090766Z","submitted_at":"2025-06-02T03:16:19Z","title":"GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.01277","snapshot_observed_at":"2026-08-06T23:12:10.857306Z","title":"Geolocsft: Efficient visual geolocation via supervised fine-tuning of multimodal foundation models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.857306Z"},"links":{"cited_paper":"/paper/2506.01277","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:13ed3664ea89375d821721724975207337f9f931cd6835b26cdf8032f7e4fc8b","observation_id":"5da9a71f-b019-499b-aeee-179f85ab3b52","resolution":{"observed_at":"2026-08-06T23:12:10.857306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.685966Z","title":"Flexdataset: Crafting annotated dataset gen- eration for diverse applications","venue":null,"work_id":"d5881d91-0c34-4a95-afaa-b3d2cd407bd4","year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:10.982218Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:4170983f8e64f4dca1ad8e7dc8cd19565eacbeb61c438701ef2beb29d5f0d30e","observation_id":"858ddb43-31da-4e4b-9d60-887041229926","resolution":{"observed_at":"2026-08-06T23:12:12.689530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.675760Z","title":"Bisenet: Bilateral segmentation network for real-time seman- tic segmentation","venue":null,"work_id":"5bd229aa-c9a0-46ce-a17a-b46b753c31a8","year":2018},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:11.055964Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:a5d806925eb0c1b686a67a620f2f30c6723f51269777f4d9ff33ec5ef47d1b01","observation_id":"e39340ab-c186-477f-9a59-5fb753fe70ba","resolution":{"observed_at":"2026-08-06T23:12:12.679363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.07122","last_updated":"2016-04-30T18:19:37Z","snapshot_observed_at":"2026-07-06T04:37:24.552839Z","submitted_at":"2015-11-23T07:32:14Z","title":"Multi-Scale Context Aggregation by Dilated Convolutions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.07122","snapshot_observed_at":"2026-08-06T23:12:11.119246Z","title":"Multi-scale context aggregation by dilated convolutions","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:11.119246Z"},"links":{"cited_paper":"/paper/1511.07122","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:41dd0abc24e4a914a070d649e886269a023d88fb2277eee2c9ad544eaf849ecf","observation_id":"891dcca7-5cc9-48df-9346-b5201673927a","resolution":{"observed_at":"2026-08-06T23:12:11.119246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:12.665695Z","title":"Context encoding for semantic segmentation","venue":null,"work_id":"9602eea0-6bb3-4bd1-be52-8d2d6bbd64e0","year":2018},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:11.171951Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:060a12ff49fbeb28a9fdce86db1bf88902949e8b67953e268668e3fd1942c660","observation_id":"2fea1001-b408-4a98-963d-45ed2290c395","resolution":{"observed_at":"2026-08-06T23:12:12.669093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.656769Z","title":"Road extraction by deep residual u-net","venue":null,"work_id":"22ba9dcd-c3ee-40c4-9a9e-caf8a515a446","year":2018},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:11.328495Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:d206533a825aeb71ab6392f326888c4898e0855ef6c8ac5bf7f50ff3016ea9fe","observation_id":"67607682-e34d-4757-948a-8aaeaf92b720","resolution":{"observed_at":"2026-08-06T23:12:12.659914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.504657Z","title":"End-to-end remote sensing change detection of unregistered bi-temporal images for natural disasters","venue":null,"work_id":"c77d9cee-c91a-43a5-9b8d-bb7b73288c82","year":2023},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:11.374555Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:47901d483df2eb28c9aafbb8fa88b666fa2aa1d09d3bbab9ed7a59ffc77761c4","observation_id":"5627d22d-9e9c-471c-91e9-bef54383b15c","resolution":{"observed_at":"2026-08-06T23:12:12.625625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.315087Z","title":"Icnet for real-time semantic segmentation on high-resolution images","venue":null,"work_id":"30c0869a-241b-443d-bfb5-6799667d1913","year":2018},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:11.485416Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:64c07a363a2f5509c75ca3a7d1390cc2882d60df280ee8be2d29a694f7d15816","observation_id":"c8cf4d0a-1e66-49c2-b701-ac86b6975f46","resolution":{"observed_at":"2026-08-06T23:12:12.398276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T23:12:12.080814Z","title":"Cooperative connection transformer for remote sensing image captioning","venue":null,"work_id":"aae4703d-1ac4-47f8-9092-893a137a4689","year":2024},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:11.591008Z"},"links":{"citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:7aec7153319ef46d6673ea0e8824225a72b8de0b82f7b2c1860cfc351789fe9f","observation_id":"d90c5507-8e1a-4305-9005-44e6a9057cd4","resolution":{"observed_at":"2026-08-06T23:12:12.193036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.11265","last_updated":"2025-03-14T10:19:24Z","snapshot_observed_at":"2026-08-07T17:04:25.768207Z","submitted_at":"2025-03-14T10:19:24Z","title":"DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.11265","snapshot_observed_at":"2026-08-06T23:12:11.687547Z","title":"Dynrsl-vlm: Enhancing au- tonomous driving perception with dynamic resolution vision-language mod- els","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:11.687547Z"},"links":{"cited_paper":"/paper/2503.11265","citing_paper":"/paper/2506.19406"},"observation_digest":"sha256:74b050873822ec2ec8628ce89bde41e381d3d3a633b71b1b5a3e2459f9d2cf41","observation_id":"edf7276f-2e30-44ec-a024-aa98c6b1ed53","resolution":{"observed_at":"2026-08-06T23:12:11.687547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.19406","last_updated":"2025-06-24T08:20:08Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T23:05:02.113195Z","submitted_at":"2025-06-24T08:20:08Z","title":"A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":47},"total_outbound_references":65},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2506.19406."}