{"as_of":"2026-08-09T14:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b7d153080fa4407c5638373660625415c02a7255e3efddeac588d8bcf1534de2","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T08:27:13.945810Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.28922/citation-record","integrity":"/paper/2606.28922/integrity","json":"/paper/2606.28922/citation-record.json","paper":"/paper/2606.28922"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T20:57:38.800121Z","title":"A low-cost polarimetric radar system based on mechanical rotation and its signal processing[J].IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(2): 4744– 4765","venue":null,"work_id":"af6a1873-6c4c-4323-8152-d2d54502d314","year":2025},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:b40baa3498b43389ad548c033f811cc598080ca2d30ea08582910723e1c93d62","observation_id":"7811b82a-da83-4fbd-bd94-34be101fd5b0","resolution":{"observed_at":"2026-07-10T20:57:38.813460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.375456Z","title":"PolSAR ship detection based on superpixel-level contrast en- hancement[J].IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1–5","venue":null,"work_id":"cb990257-37c3-4303-af37-7ab9eba4b2cc","year":2024},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:27feceb1a7062bbb3e58d4d0c05470c472e72187d68387fdab00e7c0262453db","observation_id":"18735dad-77be-4ce8-ab54-b3e8bbdbde25","resolution":{"observed_at":"2026-07-10T20:47:35.376866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.399783Z","title":"Generative adversarial nets[C]//Proceedings of Advances in Neural Information Processing Systems","venue":null,"work_id":"44c1b420-f096-4e3a-97f7-0147667fc931","year":2014},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:7aae2ea679ff1822f648bf4772f9485b15dd84db81efb3049c8ea6cfa67c155d","observation_id":"ffcb4f68-4915-4746-a4bc-a25e37e1c825","resolution":{"observed_at":"2026-07-10T20:47:35.401000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:57:38.783730Z","title":"Denoising diffusion probabilistic models[C]//Proceedings of Advances in Neural Information Processing Systems","venue":null,"work_id":"b835c1b9-e99d-4ce8-a24b-58c53293996f","year":2020},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:8fded8d6a24735f06df034ff55c9cdc6e844f9f02442e210f0b876163bf94976","observation_id":"40e45780-2f26-4583-9344-304b52395217","resolution":{"observed_at":"2026-07-10T20:57:38.797477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:57:38.753603Z","title":"Pixel recurrent neural net- works[C]//Proceedings of the 33rd International Conference on Machine Learning","venue":null,"work_id":"6ad2f415-7efc-4154-92d9-79cf5ebb1b42","year":2016},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:1072ffdc4e176ee92bf72f51070a8682ac825ed5758e2ae6e1da43dffb50a9cf","observation_id":"7aeadd9f-9f7f-4da2-a890-a6c118ddcf66","resolution":{"observed_at":"2026-07-10T20:57:38.767392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:57:38.769221Z","title":null,"venue":null,"work_id":"94a5982e-18b0-4d74-a385-8ea58607a613","year":2024},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:3998f91352087f14ac3db4597f9a0d8f41d8e7d7d9defa2a6fef66433091108c","observation_id":"96d53d5c-b403-4a4f-a581-3935e605434d","resolution":{"observed_at":"2026-07-10T20:57:38.781700Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:34.481636Z","title":"Few-shot class-incremental SAR target recognition via orthogonal distributed features[J].IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(1): 325–341","venue":null,"work_id":"67c30f70-af0f-4163-9fb5-405067a8f21f","year":2025},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:6a8302e6b11e28bad4d40044935ffa7bab5ec29a103348cdc64b8c0a2c7a6efe","observation_id":"5b1ab787-c9b6-4fb2-b6bf-b43d13e54db8","resolution":{"observed_at":"2026-07-10T20:47:34.483100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:34.483859Z","title":"Fast SAR image segmentation with deep task-specific superpixel sampling and soft graph convolution[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1–16","venue":null,"work_id":"5841fd2c-ebe0-4625-999f-e12546a72db2","year":2022},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:494b3f3638daedbdf1f81f903fec67b22a4d8185cac5ff8068984a85a8b691c1","observation_id":"e547aea6-2102-4eba-9bd7-1667b5833815","resolution":{"observed_at":"2026-07-10T20:47:34.485186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:34.485930Z","title":null,"venue":null,"work_id":"ac0f0da0-70c4-49a0-b396-128619da1ac9","year":2019},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:799204e365b8c432744ca9e72ffb9470bbcf4582b215d7ef74c81479c2c1a7b9","observation_id":"3313da12-1819-4340-a1e5-93d55d6981d7","resolution":{"observed_at":"2026-07-10T20:47:34.487055Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:34.470983Z","title":null,"venue":null,"work_id":"4f76ade1-f2d2-48d0-a7fa-09e4c47e8b79","year":2020},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:8089c4bd5b7b95846ec7f9796eff47145f3d877cc70f416c78bf195a7d8771fd","observation_id":"2e9cee9a-0d55-4b91-9bea-2b2067949127","resolution":{"observed_at":"2026-07-10T20:47:34.472259Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:34.473118Z","title":"Unpaired image-to-image translation using cycle-consistent ad- versarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision","venue":null,"work_id":"8aa68a1e-31f8-4c58-b099-3a62b8265618","year":2017},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:fa9f64f221e56b1bd8d97413aaba85b71ee62156d7a89183e1572618d1e3ab3f","observation_id":"94af8590-f951-434c-a7f6-13db49f85eda","resolution":{"observed_at":"2026-07-10T20:47:34.474610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:34.475278Z","title":"Image-to-image translation with conditional adversarial net- works[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":"00f462ae-f8d7-4f0b-a9e5-42d91b85162c","year":2017},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:a7f48d66d4e4f5a37fd46cf62ab14e42f081fa0e70b254b011363683a4e9c2f7","observation_id":"844f5e00-168c-4620-a7b0-d07b1fdba56e","resolution":{"observed_at":"2026-07-10T20:47:34.476786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:34.477543Z","title":"Optical-to-SAR image translation via neural partial differential equa- tions[C]//Proceedings of the 31st International Joint Conference on Artificial Intelligence","venue":null,"work_id":"8c42c8ea-9bdf-480d-b9e8-ad6bfa792e69","year":2022},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:e3f43c13eca66c5dd2c52769e5d4e847d0ee529b55f2553457b11b983f71ec41","observation_id":"2a14494c-a953-4c79-8e2f-c60f29bda08f","resolution":{"observed_at":"2026-07-10T20:47:34.478867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.01569","last_updated":"2018-07-04T13:29:14Z","snapshot_observed_at":"2026-07-06T06:48:20.517989Z","submitted_at":"2018-07-04T13:29:14Z","title":"The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion","version":1},"cited_work":{"arxiv_id":"1807.01569","doi":"10.48550/arxiv.1807.01569","metadata_source":"pith","pith_arxiv_id":"1807.01569","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion","venue":"cs.CV","work_id":"86fe6e38-7d24-4ebf-83ea-0b9b3407ea80","year":2018},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"cited_paper":"/paper/1807.01569","citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:1dfffde4e9d3cd5e4a214b74c86ee4f695ba0a9b1c2340f0bedebb431ee6c504","observation_id":"92403a88-9171-4757-879c-9191e214646f","resolution":{"observed_at":"2026-06-30T08:34:27.300272Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.401545Z","title":"Contrastive learning for unpaired image-to-image transla- tion[C]//Proceedings of Computer Vision–ECCV 2020","venue":null,"work_id":"8d9f41c9-c625-46db-9384-c11e89135434","year":2020},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:d1a313719c650bf09a13fa98989e4e23c6e18e319ca91b28d7957951fa35eeef","observation_id":"bffed236-6ed1-41ea-8c8e-972a2a598ab6","resolution":{"observed_at":"2026-07-10T20:47:35.402841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.397513Z","title":"StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":"7a7f16c9-b410-4900-84d3-f6fd86bc171f","year":2018},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:dbf571defc58c7850c0c452adf39cb978e07e5d1a65708693f480ad811696439","observation_id":"19629dd9-3e09-4e50-88a7-ee5f5c842485","resolution":{"observed_at":"2026-07-10T20:47:35.399017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.389277Z","title":"SAR-to-SAR image translation for domain adaptation in SAR ship detection[J].Remote Sensing, 2020, 12(16): 2607","venue":null,"work_id":"d2c9d1f7-9826-41c7-ba70-64b5bf70ecd5","year":2020},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:516d02d52781b89bf954b8c22a50def8b6edc4656fb4135033be4cadf560debc","observation_id":"66211080-8080-4802-b687-d789c5bbcdbd","resolution":{"observed_at":"2026-07-10T20:47:35.390858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.48550/arxiv.2504","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T16:58:42.353763Z","title":"Asurveyonhypothesisgenerationforsci- entific discovery in the era of large language models","venue":null,"work_id":"02811449-7be8-4587-9c0e-bb9d2479d522","year":2025},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:e3e34344cceb6ee7b5c3f5b75472316d16447bb72304a5ff5fe0bc3d3304bded","observation_id":"50ee61e4-904b-4cad-ae7e-41f55f1589f6","resolution":{"observed_at":"2026-06-30T08:34:27.294822Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-07-14T00:49:50.334573+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T00:49:50.334573+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.379840Z","title":"Selftok: A self-tokenization method for visual-language model pre- training[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":"698997a2-da4b-4357-b95c-21cf2276d584","year":2024},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:fb050ebfac9314f332cb63c39ab63581bacb4cc1a1ab7e140e003ac353a81b92","observation_id":"309baf9c-a968-491d-8ed4-a5fce4d31b65","resolution":{"observed_at":"2026-07-10T20:47:35.381608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.391635Z","title":"Parallel autoregressive generation[C]//Proceedings of the 41st International Conference on Machine Learning","venue":null,"work_id":"55d08c5d-e871-42b4-84e1-eb3aa9bb5aa6","year":2024},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:a595a236c96ede51e75e9e9e93f6aff708e101da3795f139fed77582df47cf5f","observation_id":"f6e69e1a-3d65-4d18-8f1f-6ccc2563ce25","resolution":{"observed_at":"2026-07-10T20:47:35.393013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.387166Z","title":"SAR image synthesis with diffusion models[C]//Proceedings of 2024 IEEE Radar Conference","venue":null,"work_id":"347f1326-0edf-44a0-b2e2-0fbd7b7877d2","year":2024},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:e8ce11339bd137ded0a5ed9a446ea073f953d93f3b020f3c98c8244b14d1ae3e","observation_id":"285f2ebd-89dd-4705-abd9-edb0c4759a45","resolution":{"observed_at":"2026-07-10T20:47:35.388497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.385194Z","title":"DiffuSAR: Frequency domain-aware diffusion model for SAR image generation[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 8202–8215","venue":null,"work_id":"d76edafe-b9eb-4c71-a6fb-bc2d23bf9270","year":2024},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:111a20bc47a7b111c36ea4d3b1d05560b153f32e5e95997d7e74c99e1dddbd99","observation_id":"f0f49a78-f91d-4d6f-966e-872a7f95a0bb","resolution":{"observed_at":"2026-07-10T20:47:35.386581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.393754Z","title":"DiffDet4SAR: Diffusion-based aircraft target detection network for SAR images[J].IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1–5","venue":null,"work_id":"946a0f4c-9442-4098-824f-43307df7825d","year":2024},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:de1247f074e832751872e7afc8ad7a1c866c8cb52e96e056bffe441104273e00","observation_id":"a65e3e30-8342-4322-99ef-d68047ce50c9","resolution":{"observed_at":"2026-07-10T20:47:35.395102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03206","last_updated":"2024-03-05T18:45:39Z","snapshot_observed_at":"2026-07-06T17:40:01.975792Z","submitted_at":"2024-03-05T18:45:39Z","title":"Scaling Rectified Flow Transformers for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":"2403.03206","doi":"10.48550/arxiv.2403.03206","metadata_source":"pith","pith_arxiv_id":"2403.03206","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Scaling Rectified Flow Transformers for High-Resolution Image Synthesis","venue":"cs.CV","work_id":"4dc55d76-271e-42dd-878f-c20546599c69","year":2024},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"cited_paper":"/paper/2403.03206","citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:50caafa5fda1ade3808c06e2edcc34d41919dfafb492562d3b4a53d5a5e1e36d","observation_id":"87989e0a-c71b-46a4-8836-271b1b676440","resolution":{"observed_at":"2026-06-30T08:34:27.297797Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-07-09T11:19:49.764146+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-09T11:19:49.764146+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.373621Z","title":"LoRA: Low-rank adaptation of large language mod- els[C]//Proceedings of International Conference on Learning Representations","venue":null,"work_id":"a587bfc6-6bf8-43b0-8fac-c346251f0a17","year":2022},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:9e9d7db337dc2bb4e8eccc96b65a9e1281d538646d534d890a31a55a939fa5f9","observation_id":"d5098dec-d752-428c-bf04-e8891a94114d","resolution":{"observed_at":"2026-07-10T20:47:35.374887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08465","last_updated":"2023-04-17T17:42:19Z","snapshot_observed_at":"2026-07-06T15:16:38.500909Z","submitted_at":"2023-04-17T17:42:19Z","title":"MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing","version":1},"cited_work":{"arxiv_id":"2304.08465","doi":"10.48550/arxiv.2304.08465","metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08465","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2304.08465 (2023) 5","venue":"arXiv (Cornell University)","work_id":"1f3c4847-dba4-4459-a580-4f089a14f68c","year":2009},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"cited_paper":"/paper/2304.08465","citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:8777fd5fd62313210741cdd926a923227e927e3fc06976130ce03d97041b1b6d","observation_id":"7a0e01a2-0a0f-468f-b5a3-9ecb4519ac8f","resolution":{"observed_at":"2026-06-30T08:34:27.304206Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.377439Z","title":"Flow straight and fast: Learning to generate and reconstruct with rectified flow[C]//Proceedings of the 11th International Conference on Learning Representations","venue":null,"work_id":"de0c0663-bec9-4202-bc80-990a5a29f979","year":2023},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:e63902e76de453745e36219db70d10e1f610170afd88af36b3fccdb89cff9f1e","observation_id":"a49c51a1-78a3-4a55-9a39-db25ab793c58","resolution":{"observed_at":"2026-07-10T20:47:35.379159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:57:38.768909Z","title":"Scattering characteristics guided network for ISAR space target component segmentation[J].IEEE Geoscience and Remote Sensing Letters, 2025, 22: 4009505","venue":null,"work_id":"348c0a0a-979d-4a50-b955-bbe9610466aa","year":2025},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:1fff22771a09da3837f2c51d1c3cf00cb1645dc8e6df6011d4c4a84bb86aeac4","observation_id":"5250cb7b-b8dd-4647-88e1-94a54f48e383","resolution":{"observed_at":"2026-07-10T20:57:38.782424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.382889Z","title":"Fast task-specific region merging for SAR image segmentation[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5222316","venue":null,"work_id":"84044796-6e1b-413a-b8ee-0afcf9a5d33c","year":2022},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:118653c26221c4b39d7c046498faef734c0f81a1a811ffd416ce449013439da9","observation_id":"b75b33f1-3762-4317-95f4-704bdbe75933","resolution":{"observed_at":"2026-07-10T20:47:35.384389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:47:35.395673Z","title":"Deep residual learning for image recogni- tion[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":"de3e8bbe-e979-4311-a217-704655ff6087","year":2016},"citing_paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-30T08:27:13.945810Z"},"links":{"citing_paper":"/paper/2606.28922"},"observation_digest":"sha256:9133ea8fdb2a545dea1072468d743e7a0ed4d217cd171bcf2ab7c71aa9959ae4","observation_id":"bb6a7c08-70e4-4ecc-9382-a94d23e2d5ca","resolution":{"observed_at":"2026-07-10T20:47:35.396970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.28922","last_updated":"2026-06-27T13:56:41Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-07T04:45:07.258228Z","submitted_at":"2026-06-27T13:56:41Z","title":"Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":3,"verified_exact":2,"verified_fuzzy":23},"total_outbound_references":30},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2606.28922."}