{"as_of":"2026-08-11T13:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c1df39f991a16b242c08eae65eab5a6440f65f65560fecf757be56dec55541b2","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T10:27:22.676730Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2607.20238/citation-record","integrity":"/paper/2607.20238/integrity","json":"/paper/2607.20238/citation-record.json","paper":"/paper/2607.20238"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:27:18.265283Z","title":"Atmospherictransmissionandthermalinertia induced blind road segmentation with a large-scale dataset tbrsd, in: Proc","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.265283Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:838ee960bb2bc9acf1d662c698834ca4587b0298bdf9e451c17178ee9ca26df1","observation_id":"ff2db643-9c88-4e11-b3ec-564fb156c327","resolution":{"observed_at":"2026-08-01T10:27:18.265283Z","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":"2026-08-01T10:27:18.318301Z","title":"Encoder-decoder with atrous separable convolution for semantic im- age segmentation, in: Proc","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.318301Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:318f1e60f64828c868b10298df9e19b12afd5a24d993c347b86cba721203c913","observation_id":"eee635a4-d931-43cc-a82d-7535b52a03c1","resolution":{"observed_at":"2026-08-01T10:27:18.318301Z","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":"2026-08-01T10:27:18.409860Z","title":"Vision transformer adapter for dense predictions, in: Proc","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.409860Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:2be2acf9446c9c0c2fe0b6a8319112cfc6c9f41054a7db2d246c32fb83e84cb3","observation_id":"b547d91a-5578-432c-ab04-28266635dad3","resolution":{"observed_at":"2026-08-01T10:27:18.409860Z","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":"2026-08-01T10:27:18.485228Z","title":"Masked-attention mask transformer for universal image segmenta- tion,in:Proc.IEEEConf.Comput.Vis.PatternRecognit.,pp.1290– 1299","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.485228Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:82d7ceeca5892bf0a76ab954ff0ec6dfbfb69ee3ecd876372c104c90bcde7197","observation_id":"31ed6fc8-a1a9-41f0-b089-75d97cd8df67","resolution":{"observed_at":"2026-08-01T10:27:18.485228Z","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":"2026-08-01T10:27:18.569473Z","title":"ViM-VQ: Efficientpost-trainingvectorquantizationforvisualmamba,in:Proc","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.569473Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:b239156f951775cd108e7135799e04ef0b48b5feb80757cf95466564dc0226be","observation_id":"68e0748c-5880-4881-9976-e33fe7d6c95c","resolution":{"observed_at":"2026-08-01T10:27:18.569473Z","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":"2026-08-01T10:27:18.623725Z","title":"Lraf-net: Long-range attention fusion network for visible–infrared object detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.623725Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:4fa9754f20707ab0b700cedb73d6de5bd2df6a1ee499eba59a8d8367cf632ad1","observation_id":"bb33aeff-da42-48ff-b65c-248da38f822f","resolution":{"observed_at":"2026-08-01T10:27:18.623725Z","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":"2026-08-01T10:27:18.695578Z","title":"Cf- deformable detr: an end-to-end alignment-free model for weakly Qiwei Ma et al.:Preprint submitted to ElsevierPage 11 of 13 aligned visible-infrared object detection, in: Proc","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.695578Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:ca1ce1dbea2356a631d76bd1e077e734e4030cb5437fc080051d7cfdb31df590","observation_id":"33560ead-2c15-43e6-bd59-cdfd592e5e45","resolution":{"observed_at":"2026-08-01T10:27:18.695578Z","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":"2026-08-01T10:27:18.783999Z","title":"MCMAE: Maskedconvolutionmeetsmaskedautoencoders,in:Proc.Adv.Neu- ral Inf","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.783999Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:0814a02fe51cf705b8cd2f8f592f5a7f138f14e4dfec71783ae88fceec681855","observation_id":"62a0ac14-92c2-42e8-98df-2dfb775be9b9","resolution":{"observed_at":"2026-08-01T10:27:18.783999Z","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":"2026-08-01T10:27:18.849437Z","title":"Imagebind:Oneembeddingspacetobindthemall,in: Proc","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.849437Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:b534d3e613f24a87145672e2768a391f6965c7b04cebb61f3799bd08125b9983","observation_id":"064330e3-cea5-4787-bed3-3f25c7c512b1","resolution":{"observed_at":"2026-08-01T10:27:18.849437Z","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":"2026-08-01T10:27:18.925361Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.925361Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:cbf6fc8e71dd9545a1561895de3ddea4493c7535224dfea0cde7cf0d6872f9ce","observation_id":"93da5d5b-a715-4e34-8ff8-eabc30a3a674","resolution":{"observed_at":"2026-08-01T10:27:18.925361Z","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":"2026-08-01T10:27:18.987399Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:18.987399Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:2ad271eb4d50e578b3a4545666b4d456b43e143fb9d031b61aace26e6cf78bda","observation_id":"a2bf234c-2e72-4c9c-a0ef-089ee3787806","resolution":{"observed_at":"2026-08-01T10:27:18.987399Z","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":"2026-08-01T10:27:19.050392Z","title":"Mfnet: Towards real-time semantic segmentation for autonomous vehicleswithmulti-spectralscenes,in:Proc.Int.Conf.Intell.Robots Systems, pp","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.050392Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:0448025fc54824fcd41c9cadcca02d3c69225f6a74d259988f3424ae449dc110","observation_id":"87157311-d29a-45bf-8834-83a9265bc39c","resolution":{"observed_at":"2026-08-01T10:27:19.050392Z","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":"2026-08-01T10:27:19.116614Z","title":"Masked autoencoders are scalable vision learners, in: Proc","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.116614Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:6831e43e0306ffe44853b5f000987f99683c831b79f31c2797422af0062b4214","observation_id":"345eff99-a428-4a86-9f31-eb4cb1fc72f5","resolution":{"observed_at":"2026-08-01T10:27:19.116614Z","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":"2026-08-01T10:27:19.193579Z","title":"MaskR-CNN,in: Proc","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.193579Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:97cad909513645dba222459861b5d62f98a515b478a9716df81cb77916fc1e9c","observation_id":"0ebae3f0-005f-4788-883d-ecc7c77fe3c9","resolution":{"observed_at":"2026-08-01T10:27:19.193579Z","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":"2026-08-01T10:27:19.269867Z","title":"Global–local feature fusion networkforvisible–infraredvehicledetection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.269867Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:f47512268861086ad3f996e126d68d68db20d87d3001101cea3bdf88e75f64f3","observation_id":"be8ea54c-377d-47c9-a520-ba96e70005de","resolution":{"observed_at":"2026-08-01T10:27:19.269867Z","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":"2026-08-01T10:27:19.349071Z","title":"Configuring data augmentations to reduce variance shift in positional embedding of vision transformers, in: Proc","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.349071Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:a6b67b859ae8ae8da84ae06a976f2d060bb360afc7a0974896fae62639370e73","observation_id":"33bc0292-05b2-4825-afd0-6c4ccaea521d","resolution":{"observed_at":"2026-08-01T10:27:19.349071Z","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":"2026-08-01T10:27:19.425799Z","title":"Segmentingobjects in day and night: Edge-conditioned cnn for thermal image semantic segmentation.IEEETrans.Neural.Netw.Learn.Syst.32,3069–3082","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.425799Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:268c99946bc265e880414f3a9dfe4218037656e1a889674c67eb963fcdf617dc","observation_id":"1bb4080d-8076-4cb6-ae22-bb8eb91986ca","resolution":{"observed_at":"2026-08-01T10:27:19.425799Z","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":"2026-08-01T10:27:19.510668Z","title":"Segmentingobjects in day and night: Edge-conditioned cnn for thermal image semantic segmentation.IEEETrans.Neural.Netw.Learn.Syst.32,3069–3082","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.510668Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:78e8bca4d2abd160bf5893be8d01ef108ed9ec1787212bf1223be25dfa9e56f8","observation_id":"09c4f0b6-ee67-4fcc-bbf4-d078f654ae80","resolution":{"observed_at":"2026-08-01T10:27:19.510668Z","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":"2026-08-01T10:27:19.614178Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.614178Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:26e3dafcb09e61ab2abb2f3c5951c630e3a1fe5767834ff25547dd713586e79a","observation_id":"f3ed058b-7bbc-403f-8d0e-775424f39fad","resolution":{"observed_at":"2026-08-01T10:27:19.614178Z","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":"2026-08-01T10:27:19.745491Z","title":"Explicit attention-enhanced fusion for rgb-thermal perception tasks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.745491Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:a206f083e14ebf00130e2d2181caa73d2f73151cc4bd6c46e87964ee79ae919e","observation_id":"7c004698-9d54-4fd3-9089-04b094b1d160","resolution":{"observed_at":"2026-08-01T10:27:19.745491Z","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":"2026-08-01T10:27:19.821947Z","title":"COMO:Cross- mamba interaction and offset-guided fusion for multimodal object detection","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.821947Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:340eff7404741fe6b9ae66630b7617612554869af8a18e605ade3a0041aed539","observation_id":"d141cd76-7a91-4cff-aa25-3c1a4f06d185","resolution":{"observed_at":"2026-08-01T10:27:19.821947Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T10:27:19.898322Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.898322Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:e11e6ec82139971bd915f6572996db21246557bf32ac44281a112a39ef46be9b","observation_id":"301d9679-52a1-4449-b6fc-035a114c0e56","resolution":{"observed_at":"2026-08-01T10:27:19.898322Z","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":"2026-08-01T10:27:20.112586Z","title":"InfMAE: A foundation model in the infrared modality, in: Proc","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.112586Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:ccb1b09cdfbbc53460556eb8bc7ea68c9335d99b88777b336cd0412457db458c","observation_id":"3a0d1908-62b9-4436-8521-e99e0d725874","resolution":{"observed_at":"2026-08-01T10:27:20.112586Z","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":"2026-08-01T10:27:20.187421Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.187421Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:a0b7d79b5f945911150db3186910e7b99de6e390d94700ea599e1aefc06e488a","observation_id":"d3c9fc40-235a-4be8-b278-89d27801d598","resolution":{"observed_at":"2026-08-01T10:27:20.187421Z","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":"2026-08-01T10:27:20.269171Z","title":"Bridging rgb-t image fusion and semantic segmentation via multi-task collaborative learning","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.269171Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:e5a4cd1e0035a1befcf2aedb49d1956cc4e83ebfe027de2dec13462f4c6e356d","observation_id":"0bf4be3a-c3a1-4b6c-86ae-4f0225c30439","resolution":{"observed_at":"2026-08-01T10:27:20.269171Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T10:27:20.322005Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.322005Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:3d56718726b5d39faf6fa2589c1290f0bdfc5d1fcacde271dcc32c2ecc795bdb","observation_id":"4d1e1789-98e9-47ab-b09b-c4f0a76c867c","resolution":{"observed_at":"2026-08-01T10:27:20.322005Z","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":"2026-08-01T10:27:20.495079Z","title":"Visualizing data using t-SNE","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.495079Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:27f7e5271ed5579a1ce1dd6f93e68cb41bb536b4f063285e974864e515f29c1a","observation_id":"8f6249ea-4837-4b18-833e-f9f41fcddbe7","resolution":{"observed_at":"2026-08-01T10:27:20.495079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.15642","last_updated":"2026-05-13T09:44:42Z","snapshot_observed_at":"2026-08-09T11:33:28.649975Z","submitted_at":"2025-09-19T06:07:53Z","title":"UNIV: Unified Foundation Model for Infrared and Visible Modalities","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.15642","snapshot_observed_at":"2026-08-01T10:27:20.578105Z","title":"UNIV: Unified foundation model for infrared and visible modalities","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.578105Z"},"links":{"cited_paper":"/paper/2509.15642","citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:018b600153eaac73ac91734aab5218a8dff5710e273dc9ee1eba0f0198aacaed","observation_id":"5d2a49d0-3772-4110-b706-4cb64ef106b9","resolution":{"observed_at":"2026-08-01T10:27:20.578105Z","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":"2026-08-01T10:27:20.686848Z","title":"Suppresscontentshift:Betterdiffusionfeaturesviaoff-the-shelfgen- eration techniques","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.686848Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:dff8c1cbf5f19ad80c4fd9c0ccd61f69c3f4e9ce96f6ac45ebbd355b9aa73f20","observation_id":"97ff8a2c-1069-4011-acb2-b5762047d4bc","resolution":{"observed_at":"2026-08-01T10:27:20.686848Z","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":"2026-08-01T10:27:20.770514Z","title":"Connecting joint-embedding predictive architecture with contrastive self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.770514Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:e192f720cabc2e7d1d33c5fd4947c2c64c7000d55a1d8937b3c22f65e5f0b60e","observation_id":"6ec6c76f-e384-493d-829e-0a57f808a015","resolution":{"observed_at":"2026-08-01T10:27:20.770514Z","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":"2026-08-01T10:27:20.837725Z","title":"Attributefilterbased infrared and visible image fusion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.837725Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:63173f83854b30f4c69e3b8e920f4b922623278045ea7f5a9dc13b3c9b9b3867","observation_id":"68deaa24-f4f5-4111-8402-ebf498505d14","resolution":{"observed_at":"2026-08-01T10:27:20.837725Z","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":"2026-08-01T10:27:20.915569Z","title":"Interactive visible and infrared image fusion and segmentation","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.915569Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:ebfe2b54dd2877fd0cc6f3f44b1b0db7cc37996e604463865f1708d45b283c3c","observation_id":"54520e64-a796-42ef-96bc-e732a0d4bd15","resolution":{"observed_at":"2026-08-01T10:27:20.915569Z","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":"2026-08-01T10:27:20.993008Z","title":"Learning by aligning: Visible-infrared person re-identification using cross-modal corre- spondences, in: Proc","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.993008Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:cc8552356f6c4e10ac77f7913c8e1b837eae0cf58771d40a08cdcc3dc8f65aa7","observation_id":"d57e5170-2cb5-4f7e-8f38-39ae2e7d57a6","resolution":{"observed_at":"2026-08-01T10:27:20.993008Z","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":"2026-08-01T10:27:21.093403Z","title":"Learning transferable visual models from natural language supervision, in: Proc","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.093403Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:8fd66dd8b95f0e8259ffb77d313db9ecfdd1a47685014a04c63e3a60b3fa9d05","observation_id":"e45e87e3-4118-4ce3-bc7a-1bafd87df7de","resolution":{"observed_at":"2026-08-01T10:27:21.093403Z","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":"2026-08-01T10:27:21.185934Z","title":"Drone-based rgb- infraredcross-modalityvehicledetectionviauncertainty-awarelearn- ing","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.185934Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:086c061ee88768e13b209016a1ce7c154bdda09f177ef2bddc5fa46af2a734e0","observation_id":"e08fe7ba-3352-467b-8096-c9f70f5ad206","resolution":{"observed_at":"2026-08-01T10:27:21.185934Z","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":"2026-08-01T10:27:21.197222Z","title":"PIAFusion: A progressive infrared and visible image fusion network based on illumination aware","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.197222Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:3969b48747f2757aa11efadb9e0c83694ed38d96a1551319f8912c8b042661cd","observation_id":"1e2431d5-b060-4a5b-b87e-df83d9fd8ff6","resolution":{"observed_at":"2026-08-01T10:27:21.197222Z","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":"2026-08-01T10:27:21.295300Z","title":"Yolov8: A novel object detection algorithm with enhanced performance and robustness, in: Proc","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.295300Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:55b05171ca03e5929f4f3af7260b09d8232774a6318a8ea94002e6147caabb58","observation_id":"ab31d01c-b522-40dd-a5a9-db63bb971f22","resolution":{"observed_at":"2026-08-01T10:27:21.295300Z","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":"2026-08-01T10:27:21.419172Z","title":"Unifiedperceptual parsing for scene understanding, in: Proc","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.419172Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:6b0581e2bd337a092b13fd37554cd63f1bc1d6f5c5248fe0246353f2477c3d78","observation_id":"404d715b-7dcc-4922-9efb-94a032394fe2","resolution":{"observed_at":"2026-08-01T10:27:21.419172Z","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":"2026-08-01T10:27:21.550350Z","title":"SegFormer: Simple and efficient design for semantic segmentationwithtransformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.550350Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:2b20e55b60a7dcaed2aa586c898684342f21cafdb0e42a9e83864f9ce40dcced","observation_id":"08a489e4-a965-4bbd-a11f-bcbb2aa2d257","resolution":{"observed_at":"2026-08-01T10:27:21.550350Z","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":"2026-08-01T10:27:21.664367Z","title":"MCNet:Multi-levelcorrectionnet- work for thermal image semantic segmentation of nighttime driving scene","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.664367Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:42a6b8eaac655c7c6ac63dc7ec93817cb0c52cec0d8ce9bcd09a653405b5deb3","observation_id":"ce49cce2-9df9-4610-9309-1c4761b62027","resolution":{"observed_at":"2026-08-01T10:27:21.664367Z","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":"2026-08-01T10:27:21.750883Z","title":"Fast and robust matching for multimodal remote sensing image registration","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.750883Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:35ed59aaa1faf04cff1db6fd4fd59719ef6db05cee547d21a5972487db716458","observation_id":"c939e05a-4923-4f22-a071-64debfa929de","resolution":{"observed_at":"2026-08-01T10:27:21.750883Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T10:27:21.865518Z","title":"SPGFusion: Semantic prior guided infrared and visible image fusion via pretrained vision models","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.865518Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:76fb153b9316c136c2e172eed034d31e52f01b282d7a38d6bbe729a921fcce95","observation_id":"78c615da-a0ac-401f-8ea7-9c66682dca63","resolution":{"observed_at":"2026-08-01T10:27:21.865518Z","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":"2026-08-01T10:27:21.928737Z","title":"CR2PQ:Continuousrelativerotarypositionalqueryfordense visual representation learning, in: Proc","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:21.928737Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:e954d02f9e532810afa9ad69d8989e95ecae36fd78b2e466dd96d4fa08f5c836","observation_id":"b479e1a6-cf76-4135-ae47-abdb663ee2df","resolution":{"observed_at":"2026-08-01T10:27:21.928737Z","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":"2026-08-01T10:27:22.022033Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:22.022033Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:22658ec1e9b9c1a56b704da0e7d64b54474dcf067415e3b9b1c980780f65c2e7","observation_id":"94649f7a-e071-418f-9afe-9ef4a378fc63","resolution":{"observed_at":"2026-08-01T10:27:22.022033Z","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":"2026-08-01T10:27:22.258844Z","title":"UNIP:Rethinkingpre-trainedattentionpatternsforinfraredsemantic segmentation, in: Proc","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:22.258844Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:fa9853037a77cd549193b0ec80432057ff1221793257716c8229b930a9bf9faa","observation_id":"def11a75-4f83-4086-b1f9-48ac89f6eb86","resolution":{"observed_at":"2026-08-01T10:27:22.258844Z","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":"2026-08-01T10:27:22.363617Z","title":"IEEE Conf","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:22.363617Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:2c82f885696862677ec106d6bfc7f4f1a592c5e5979ef413913297cc7583c8a2","observation_id":"aa878abd-0705-444a-bc09-afb5f1006c32","resolution":{"observed_at":"2026-08-01T10:27:22.363617Z","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":"2026-08-01T10:27:22.431436Z","title":"Cddfuse:Correlation-drivendual-branchfeature decompositionformulti-modalityimagefusion,in:Proc.IEEEConf","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:22.431436Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:3370fcabb21beefc2b6a653dc1b751dbc1f553537a6842cb522ee960b2157486","observation_id":"2befc47d-c8a5-43e6-a4bd-080fe08beb49","resolution":{"observed_at":"2026-08-01T10:27:22.431436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08192","last_updated":"2023-12-13T14:57:28Z","snapshot_observed_at":"2026-08-11T10:06:32.539423Z","submitted_at":"2023-12-13T14:57:28Z","title":"PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.08192","snapshot_observed_at":"2026-08-01T10:27:22.146255Z","title":"arXiv preprint arXiv:2312.08192","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:22.146255Z"},"links":{"cited_paper":"/paper/2312.08192","citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:c59ec37948ed8412beb4f3eef1937c8427d2425e57ac034c3802e469bc49e77f","observation_id":"00decbcf-c16d-443d-bf09-321064fe01c3","resolution":{"observed_at":"2026-08-01T10:27:22.146255Z","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":"2026-08-01T10:27:22.676730Z","title":"Semantic understanding of scenes through the ade20k dataset","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:22.676730Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:dfb2be19cc08419c42ba43dd90328ab294d8db20518862b4ef9171af8a078dee","observation_id":"36a90e57-ddaa-4614-93dd-a6ec33365e1e","resolution":{"observed_at":"2026-08-01T10:27:22.676730Z","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":"2026-08-01T10:27:22.542679Z","title":"Equivariant multi-modality image fusion, in: Proc","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:22.542679Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:730429599eb4b8cf2010bfad6bcadf6219a881ba7b0e14e492e1d29fb3833351","observation_id":"e6f9f5bd-afb0-45c8-8ef5-0f39d4d16465","resolution":{"observed_at":"2026-08-01T10:27:22.542679Z","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":"2026-08-01T10:27:20.009555Z","title":"IEEE Trans","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.009555Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:2e1d305a7fdc5584428cef06d0e09bade5a01d4e466694bd31e6cface8ba7d70","observation_id":"1bc95a16-04b3-4cc4-85cf-b963b94b5d3f","resolution":{"observed_at":"2026-08-01T10:27:20.009555Z","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":"2026-08-01T10:27:19.690005Z","title":"IEEE Conf","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:19.690005Z"},"links":{"citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:cfe0fb2c4ea3872ccb0911f6e681925f7384684a8579cf85f1fb1f4688a752e0","observation_id":"e31b036c-36df-45b5-9fd3-554a3b9de4d2","resolution":{"observed_at":"2026-08-01T10:27:19.690005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.22665","last_updated":"2026-08-08T09:11:43Z","snapshot_observed_at":"2026-08-11T13:16:55.703376Z","submitted_at":"2025-10-26T13:04:50Z","title":"SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.22665","snapshot_observed_at":"2026-08-01T10:27:20.420271Z","title":"arXivpreprint arXiv:2510.22665","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-01T10:27:20.420271Z"},"links":{"cited_paper":"/paper/2510.22665","citing_paper":"/paper/2607.20238"},"observation_digest":"sha256:c87b45a88247bda89c86028b13a800521327647f4cc89fb3b22d7ac262703ad4","observation_id":"079263cd-40ce-46ee-95bf-fd2d8cb81b38","resolution":{"observed_at":"2026-08-01T10:27:20.420271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.20238","last_updated":"2026-07-22T14:59:02Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T20:22:50.050480Z","submitted_at":"2026-07-22T14:59:02Z","title":"Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":3,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":50,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":53},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2607.20238."}