{"as_of":"2026-08-08T09:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4176a9f3002a076876ef8b5ebde5200dd902a2b353e8265d9762c67490a10643","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:20:58.860481Z","state":"measured"},{"denominator":60,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":60,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T22:28:35.155099Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T22:34:01.822775Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"cited_work":{"arxiv_id":"2506.02843","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.02843","snapshot_observed_at":"2026-06-29T22:34:01.822775Z","title":"Random registers for cross-domain few-shot learning.arXiv preprint arXiv:2506.02843, 2025","venue":null,"work_id":"cdc633e2-f273-44f6-82c2-1d5ff652ba26","year":2025},"citing_paper":{"arxiv_id":"2605.25799","last_updated":"2026-05-25T12:49:15Z","snapshot_observed_at":"2026-07-06T23:35:41.527169Z","submitted_at":"2026-05-25T12:49:15Z","title":"Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T22:28:35.155099Z"},"links":{"cited_paper":"/paper/2506.02843","citing_paper":"/paper/2605.25799"},"observation_digest":"sha256:ae7efa1d10ce956f7a4d05570910ccb7cd1bff26238089f0bec39eef94c4ad79","observation_id":"fc0ad6da-765a-4e80-9d66-b5c66fd7d999","resolution":{"observed_at":"2026-06-29T22:34:01.825461Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"cited_work":{"arxiv_id":"2506.02843","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.02843","snapshot_observed_at":"2026-06-29T22:34:01.822775Z","title":"Random registers for cross-domain few-shot learning.arXiv preprint arXiv:2506.02843, 2025","venue":null,"work_id":"cdc633e2-f273-44f6-82c2-1d5ff652ba26","year":2025},"citing_paper":{"arxiv_id":"2605.29776","last_updated":"2026-05-28T11:21:44Z","snapshot_observed_at":"2026-08-03T05:52:15.988085Z","submitted_at":"2026-05-28T11:21:44Z","title":"Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T08:16:57.329872Z"},"links":{"cited_paper":"/paper/2506.02843","citing_paper":"/paper/2605.29776"},"observation_digest":"sha256:90eaa9dfe6554b79f0142144e49d1deb8d54555f9b570099d55148f4d3f3f295","observation_id":"b6b5343f-4a52-4078-bec2-8ce0c7353947","resolution":{"observed_at":"2026-06-29T08:23:15.556445Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.02843/citation-record","integrity":"/paper/2506.02843/integrity","json":"/paper/2506.02843/citation-record.json","paper":"/paper/2506.02843"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.677097Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.677097Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:a09337b990300b188c2321695dbbd4dde3677f0d3ff36843848d440502398a1e","observation_id":"af4d670d-994f-442b-902d-3e7e3108499b","resolution":{"observed_at":"2026-08-07T11:20:58.677097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.576645Z","title":"Accumulated trivial attention matters in vision transformers on small datasets","venue":null,"work_id":"694abb04-f757-45ad-810d-0ba5152d5063","year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.681461Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:05603b25c116edd5b4f845edd44c05433f361e3124002173b120d98e588a4917","observation_id":"66a63f22-4bc0-4ee4-9b51-150dec2a919f","resolution":{"observed_at":"2026-08-07T11:20:59.580344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.567725Z","title":"On separate normalization in self-supervised transformers, 2023 b","venue":null,"work_id":"90ac265a-b095-476c-8f8a-ffb24225ce13","year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.684746Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:a6b606988597bd0c261c5292574212b5e8fa7bd4bd4b05ebd24b2c11e269f06b","observation_id":"d8ec8338-aff6-406b-bee0-c99dce8a7db5","resolution":{"observed_at":"2026-08-07T11:20:59.571086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.558947Z","title":"Meta-baseline: Exploring simple meta-learning for few-shot learning, 2021","venue":null,"work_id":"c230ae7d-4b78-4f14-adaa-e39a433c0269","year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.688241Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:47129b651a164f1285b434184e962c217cfcfbcd765febb369ebcebbf2357a58","observation_id":"87461e24-3871-4996-ad4b-d68ae7b5fdd1","resolution":{"observed_at":"2026-08-07T11:20:59.562181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.691772Z","title":"E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H., and Halpern, A","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.691772Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:e046df053e5416c263f165295cd1a09ee187d669ed8028f1b0c3dda22c043d2d","observation_id":"24010e01-8737-45a1-8407-200ae919912d","resolution":{"observed_at":"2026-08-07T11:20:58.691772Z","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":"2026-08-07T11:20:58.695656Z","title":"Vision transformers need registers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.695656Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:40fd607ec3e9525991961afe6d311e733bb85a306d91422f15887543b8c1d3db","observation_id":"1a63f55e-e48f-463c-b35a-ce827043f88e","resolution":{"observed_at":"2026-08-07T11:20:58.695656Z","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":"2026-08-07T11:20:58.699799Z","title":"Confess: A framework for single source cross-domain few-shot learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.699799Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:7a6d57141c17590fb7c6caf83e6fe9cdad704faf633955aa1e55d74e2cd105b8","observation_id":"29d1fb0b-a952-422f-8931-628adba57a18","resolution":{"observed_at":"2026-08-07T11:20:58.699799Z","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":"2026-08-07T11:20:58.702962Z","title":"Reliability of cka as a similarity measure in deep learning, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.702962Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:64b643b837037f52f97d030e9be063e1d7854b3225159bd3c6ed2a41253dceff","observation_id":"547861d4-a844-48c6-a02c-7db4917d3837","resolution":{"observed_at":"2026-08-07T11:20:58.702962Z","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":"2026-08-07T11:20:58.706957Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.706957Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:776885bd0a5991a946911dcdd85912e7d717a206bde903604b5049b21909700c","observation_id":"a442ff2b-7341-4571-9375-9d01dde78903","resolution":{"observed_at":"2026-08-07T11:20:58.706957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.526173Z","title":"Sharpness-aware minimization for efficiently improving generalization, 2021","venue":null,"work_id":"71459ad7-db25-4ba6-bd24-3d4d5a8eaf35","year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.709858Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:4efa9fd1e470e8c894566a08699cf4cd3756aa5138bb2432a0031b8e74b72d39","observation_id":"ba49c06d-d5bf-41a2-80c7-099fb9eb3240","resolution":{"observed_at":"2026-08-07T11:20:59.529186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.713146Z","title":"Meta-fdmixup: Cross-domain few-shot learning guided by labeled target data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.713146Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:14f5a008e58b7cbcc7436a75968cdda0fb4b425323c8a6b92f9b1d3a4d174c21","observation_id":"c2ae84fe-1201-4d61-93a7-a4dc7ddd5040","resolution":{"observed_at":"2026-08-07T11:20:58.713146Z","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":"2026-08-07T11:20:58.716155Z","title":"Wave-san: Wavelet based style augmentation network for cross-domain few-shot learning, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.716155Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:2fae8fc3b4915d0ad6840a2977f15f1c8ee8ed636f243e9a3f1bef18e823cb29","observation_id":"bb3dcb6b-c1af-4384-beed-dfa262c1764b","resolution":{"observed_at":"2026-08-07T11:20:58.716155Z","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":"2026-08-07T11:20:58.719073Z","title":"Styleadv: Meta style adversarial training for cross-domain few-shot learning, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.719073Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:b5f7c72120e396b1272621c26ebe95aca0724bf05813c1c76f10ecf0f7b422cd","observation_id":"a4380a2c-74bf-4bf5-a5f8-e12ab6dea077","resolution":{"observed_at":"2026-08-07T11:20:58.719073Z","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":"2026-08-07T11:20:58.721931Z","title":"C., Karlinsky, L., Codella, J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.721931Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:beb32645969ca44d0c05e23b98572da09b78a8f444d92fa70a15ef458c60d043","observation_id":"4d9de87c-6df2-4983-a08d-d4b5625c0b00","resolution":{"observed_at":"2026-08-07T11:20:58.721931Z","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":"2026-08-07T11:20:58.724843Z","title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.724843Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:0d198e497a2308ab92ada9bd1670b70dff8f478aa8a72ce2c889f502ec739591","observation_id":"7bd1cef3-c2e1-4c74-a7f4-5606bd90f0b2","resolution":{"observed_at":"2026-08-07T11:20:58.724843Z","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":"2026-08-07T11:20:58.727709Z","title":"and Ma, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.727709Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:b0add68c6c668c383c14b90d373cb755c5327c3b6d891d1ad536ca72c118cbe1","observation_id":"009f90de-c117-4a19-b0f9-d775c6421860","resolution":{"observed_at":"2026-08-07T11:20:58.727709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.489452Z","title":"Visual prompt tuning","venue":null,"work_id":"b88aa3a4-a96d-4ef6-ae0e-2d4053d8ac4f","year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.730635Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:b3dc92b8ebcc741206029fb04cf873e64553b7a8c08a244994f3c50cad33c6be","observation_id":"36619385-5b50-4326-a932-a6007eec7f54","resolution":{"observed_at":"2026-08-07T11:20:59.492393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.480321Z","title":"and Han, B","venue":null,"work_id":"50abfaff-2412-40d3-be96-0eda3275911e","year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.733609Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:cc01d845be90e303dffd3e81a602b819541addd2a0f37381944fba844a88134b","observation_id":"c8ebdb9f-d3e3-415d-b388-a48bacce434b","resolution":{"observed_at":"2026-08-07T11:20:59.483945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.736437Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.736437Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:2a7b90ba870adf92cadb84e2c9a9627a8fa38406e307fa7823e59171ac5b7490","observation_id":"c2ff2a53-d1c8-4bbd-944e-159655b34d98","resolution":{"observed_at":"2026-08-07T11:20:58.736437Z","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":"2026-08-07T11:20:58.739816Z","title":"Similarity of neural network representations revisited","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.739816Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:88fea1ad91650865e23dcc3a23fb02cbac174bb40425db6db77e89c36fef0c71","observation_id":"47f44b2b-3c5a-4e31-82a5-7c1047c45edf","resolution":{"observed_at":"2026-08-07T11:20:58.739816Z","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":"2026-08-07T11:20:58.742693Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.742693Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:9e121d9e3ae2cc6c255036e21f2613b14123da512d556c3cfe63bc943b5a45f5","observation_id":"fd1d393c-9268-439f-8822-f9f999827399","resolution":{"observed_at":"2026-08-07T11:20:58.742693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.454700Z","title":"Adversarial feature hallucination networks for few-shot learning","venue":null,"work_id":"fbd49619-ecac-41ed-a8eb-fedfa1d34b85","year":2020},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.745656Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:e102eb37b9ffa893a32e517aba87761dc137765415f6f90cc421e769e46fda8a","observation_id":"6ebb6dd1-9e14-437a-9545-f6e2135261b9","resolution":{"observed_at":"2026-08-07T11:20:59.459983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.748790Z","title":"Ranking distance calibration for cross-domain few-shot learning, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.748790Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:5669edbcf2906491c5333e761bca83ddee5150cc6a22163724bce9dd034fb926","observation_id":"197857bb-8332-4b3e-aa91-a5dbdd50c327","resolution":{"observed_at":"2026-08-07T11:20:58.748790Z","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":"2022.10866","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.276933Z","title":"Learning multi-level weight-centric features for few-shot learning","venue":null,"work_id":"d91a1e1b-7bc3-49d2-864b-8a158a73ce46","year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.751474Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:dbc11733614f0519e33b1118d091043b9b14327587c86a8c5f0aca964ced35f4","observation_id":"32b338af-d5fe-4773-b9e8-c1f397b56432","resolution":{"observed_at":"2026-08-07T11:20:59.284293Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.440575Z","title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","venue":null,"work_id":"b41b9a3d-f62b-4fc8-b4ba-7c7882906ad3","year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.754324Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:b2a9af4c8286c163f57ce78764439be4c9fb8e9297888ac46c550a73e470850d","observation_id":"d1a813ea-5605-4379-af5f-0d841ea115ab","resolution":{"observed_at":"2026-08-07T11:20:59.444488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.757742Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.757742Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:083484d168605af17298ad6ff67decba55b3464cd7f603e11675f0892b1e7df7","observation_id":"8b9f9208-4582-4036-a3be-9093d04ba146","resolution":{"observed_at":"2026-08-07T11:20:58.757742Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19101","last_updated":"2024-12-26T07:43:01Z","snapshot_observed_at":"2026-07-06T20:13:17.794908Z","submitted_at":"2024-12-26T07:43:01Z","title":"Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19101","snapshot_observed_at":"2026-08-07T11:20:58.760619Z","title":"Reconstruction target matters in masked image modeling for cross-domain few-shot learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.760619Z"},"links":{"cited_paper":"/paper/2412.19101","citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:ea1abe88c8a6116209c408bad7ebb256755f870ac2983c441af9b9861f91e807","observation_id":"df2303d5-507c-498c-88b3-031c071eb3b3","resolution":{"observed_at":"2026-08-07T11:20:58.760619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.426812Z","title":"Prod: Prompting-to-disentangle domain knowledge for cross-domain few-shot image classification","venue":null,"work_id":"00c49055-e086-41a0-b9e6-bae076f32534","year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.764743Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:067f0a4e8d9532c556d949baa95a0a6d4e538f7a360ff4c1538d37ab8a6afa4e","observation_id":"ff937e1a-d806-4aa2-a1b0-ee19e2072640","resolution":{"observed_at":"2026-08-07T11:20:59.430275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.767959Z","title":"Using deep learning for image-based plant disease detection","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.767959Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:40a87b62300df6683eca8285d6457a8f01f4cb31573e4565094151728ac596d1","observation_id":"0ed63c6a-f9c2-4320-a671-8e7f994ed73c","resolution":{"observed_at":"2026-08-07T11:20:58.767959Z","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":"2026-08-07T11:20:58.770961Z","title":"M., Ranasinghe, K., Khan, S","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.770961Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:ffc2aad372b625f87c0f40e5c33348a28096ca420810f930207b026b5b51abf8","observation_id":"90e0905d-d5e6-48ac-981c-4d49eba862d7","resolution":{"observed_at":"2026-08-07T11:20:58.770961Z","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":"2026-08-07T11:20:58.773820Z","title":"A., Osowiechi, D., Ayed, I","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.773820Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:8b97743e4c80cafa69ab3958c667f1d800d1ed76a54ce734b539d564c8d4c6b3","observation_id":"85711286-90f5-4fa4-bcff-27cf9a48e089","resolution":{"observed_at":"2026-08-07T11:20:58.773820Z","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":"2026-08-07T11:20:58.777126Z","title":"Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.777126Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:8269c1b19bb13b6292a1c38f9b0c1a617589c4db0df50380a47399c3e6da8f8f","observation_id":"5b951dd6-b809-4a74-a748-cc6d1e3c6338","resolution":{"observed_at":"2026-08-07T11:20:58.777126Z","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":"2026-08-07T11:20:58.779985Z","title":"W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.779985Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:1c696c246ead62a8434bacc2ee465abcefcb54fafe87339d658d70fab6da7902","observation_id":"5e473722-a9ac-483a-a001-d130320c400f","resolution":{"observed_at":"2026-08-07T11:20:58.779985Z","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":"2026-08-07T11:20:58.783126Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.783126Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:46ebb635603a6c7fa564c5378000721ab24139f033b8c235845faee143a096ea","observation_id":"f9154403-fdf4-401e-bfa4-c8ae4df885ee","resolution":{"observed_at":"2026-08-07T11:20:58.783126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.398254Z","title":"Prototypical networks for few-shot learning","venue":null,"work_id":"8fd4dd73-1943-4be1-9b47-de9e54c2b411","year":2017},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.786279Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:fac46857daad48271fdf71658e446158e6f0008e8407e78b8bb968ac2cc52e8c","observation_id":"4e71ae91-f752-437b-adb7-9242bfc01fd1","resolution":{"observed_at":"2026-08-07T11:20:59.402204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.389621Z","title":"Visual prompt tuning for generative transfer learning","venue":null,"work_id":"64fcbad4-dfcf-4439-8323-2560805bc682","year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.789293Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:b1f7405c4b0bac9324bcf45773e644f755d6d29e214df18781816f654ea6d970","observation_id":"4e8a2459-2f24-4d05-a3a8-f9e837bb3b16","resolution":{"observed_at":"2026-08-07T11:20:59.392960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.792190Z","title":"Cross-domain few-shot classification via learned feature-wise transformation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.792190Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:44cb15d47fbedbecb6788456b2e1c3e5b496aeaba974bb6588b460dee3a0c549","observation_id":"7206433a-6e8b-42ac-998b-a1b1d43ced20","resolution":{"observed_at":"2026-08-07T11:20:58.792190Z","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":"2026-08-07T11:20:58.795111Z","title":"Matching networks for one shot learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.795111Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:8a511364b5e1ebddc9cb59f3d6c2553e862410e73aa92d423cb4f510f94ab703","observation_id":"ef1a8a02-3727-426c-8fe1-f65ac0f09108","resolution":{"observed_at":"2026-08-07T11:20:58.795111Z","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":"2026-08-07T11:20:58.798053Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.798053Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:bb872b56a125bb5f06b7232144ef7b0ab623c0c00acdd6acea3db6ffa2fe2ef2","observation_id":"16a5f384-7a4a-4acc-aa47-7aff562ae0b7","resolution":{"observed_at":"2026-08-07T11:20:58.798053Z","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":"2026-08-07T11:20:58.801180Z","title":"and Deng, Z.-H","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.801180Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:930c75df2d1a951a2265be6694e46960b2c2a89312709276ac45b1b01e1048da","observation_id":"ef10dd5e-165a-4173-9d2c-9f7dc08b1041","resolution":{"observed_at":"2026-08-07T11:20:58.801180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.366509Z","title":"Z., and Yan, S","venue":null,"work_id":"6082ec7a-efb2-48bf-b550-82601353e7e6","year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.803990Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:85c60feb45f111aa0a5c4303f73a6e2af82f120058914e5e66cc20b62e08c545","observation_id":"a4cb923b-17d1-4e83-b758-0258591af9cc","resolution":{"observed_at":"2026-08-07T11:20:59.369693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.806684Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.806684Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:9316433dfe5332f6d270da0e958abb7519a2216df13748a7d672ef2dd53a2997","observation_id":"6dc0b10b-a455-4a09-ab26-76e8ceb36d1d","resolution":{"observed_at":"2026-08-07T11:20:58.806684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.357291Z","title":"Efficient vision-language pre-training by cluster masking","venue":null,"work_id":"31dc7b74-8edc-4d9d-9678-0d1a692dd8ab","year":2024},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.809483Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:15fe81dabf37c4b7be243014d5bfbb90ba0f58cbfa052c3b999d96b7048416a6","observation_id":"64a66125-4218-4ca5-b59b-4a6a9fb3068b","resolution":{"observed_at":"2026-08-07T11:20:59.360382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.11382","last_updated":"2023-02-21T12:42:44Z","snapshot_observed_at":"2026-07-06T14:54:37.559648Z","submitted_at":"2023-02-21T12:42:44Z","title":"A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.11382","snapshot_observed_at":"2026-08-07T11:20:58.814316Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.814316Z"},"links":{"cited_paper":"/paper/2302.11382","citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:ec6eeb6ee9fd8b31bf96f810230fad27e54257be09257f8c56baddded6d522fa","observation_id":"6864de90-32e8-48f8-a72d-84d494aa9a54","resolution":{"observed_at":"2026-08-07T11:20:58.814316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08557","last_updated":"2025-02-18T08:34:28Z","snapshot_observed_at":"2026-07-06T15:03:38.819154Z","submitted_at":"2023-03-15T12:18:16Z","title":"Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08557","snapshot_observed_at":"2026-08-07T11:20:58.818136Z","title":"M., and Liu, L","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.818136Z"},"links":{"cited_paper":"/paper/2303.08557","citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:919e93132b276b5a67c0cf2ee6e2bba46891d4f6218c151c6b5ea03a67c3a150","observation_id":"acafd77e-7aa3-483e-b582-38a961803ffd","resolution":{"observed_at":"2026-08-07T11:20:58.818136Z","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":"2026-08-07T11:20:58.825890Z","title":"Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.825890Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:b79107f48a11bbba603fd1b2fea1eb29d002bd9a2bcb4f773f0da9a25a069b64","observation_id":"e17c6fee-7f02-414b-addd-c6927aa2a9ff","resolution":{"observed_at":"2026-08-07T11:20:58.825890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.342386Z","title":"Visual-language prompt tuning with knowledge-guided context optimization","venue":null,"work_id":"484efcc1-c637-4431-baec-d7896feb3436","year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.829856Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:d56acca9e3b2db49d41d78f85b7f3e6ed78802c5469880d6194d5e1ef7f0ad56","observation_id":"fb278f4d-ac31-4f57-9899-196308d0300b","resolution":{"observed_at":"2026-08-07T11:20:59.345540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.832379Z","title":"Delving deep into the generalization of vision transformers under distribution shifts","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.832379Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:fa9e129606bfc07b74e9343d0952379c53b1371eede4520d83e91ffd660e6ee3","observation_id":"239972cc-d9cd-43df-87e1-33f856bb8b55","resolution":{"observed_at":"2026-08-07T11:20:58.832379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.332851Z","title":"M., and Shum, H.-Y","venue":null,"work_id":"e94f11d4-f2ad-4f52-869f-aefa0a7f4d0b","year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.835487Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:2120d4002ecf5e45b02241201d50b85541c3164c27cb8df7553d45dabc0499d9","observation_id":"4f201dae-f13e-4f59-a7b3-2008719f045a","resolution":{"observed_at":"2026-08-07T11:20:59.335895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.323686Z","title":"Free-lunch for cross-domain few-shot learning: Style-aware episodic training with robust contrastive learning","venue":null,"work_id":"1c16b1ec-bb14-4fe1-8b5e-c06e56ca9281","year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.837956Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:14f64b53e5d2fa009310a3ffb41eb35744fd5f520ab86a5a9ab89b1d43ad6d28","observation_id":"b1d0ea69-3b52-4a6d-bac6-10876c266b9e","resolution":{"observed_at":"2026-08-07T11:20:59.326920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.840653Z","title":"Revisiting prototypical network for cross domain few-shot learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.840653Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:d67765dc2c5b0fd8546edbc1b79aa0b9c47d67e6c2e985bdd561adda1c99ca5c","observation_id":"c145120d-ed54-42d2-bc2a-8e681d523085","resolution":{"observed_at":"2026-08-07T11:20:58.840653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07832","last_updated":"2022-01-27T09:20:49Z","snapshot_observed_at":"2026-07-06T12:08:39.149450Z","submitted_at":"2021-11-15T15:18:05Z","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.07832","snapshot_observed_at":"2026-08-07T11:20:58.843455Z","title":"ibot: Image bert pre-training with online tokenizer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.843455Z"},"links":{"cited_paper":"/paper/2111.07832","citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:8c3585936409ad3d8cf228ebbb94c022ed3f0d85843744e9fccd569951b49edf","observation_id":"9e4e4f29-92a4-4c75-96ce-a9721d74c075","resolution":{"observed_at":"2026-08-07T11:20:58.843455Z","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":"2026-08-07T11:20:58.846464Z","title":"Attention temperature matters in vit-based cross-domain few-shot learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.846464Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:ce5cd05d887a9016d58f505f7136ab5c6c4c8c1c3295469ad7a9e2e2b6d1eb10","observation_id":"637af706-be44-4445-80de-89e2343a681a","resolution":{"observed_at":"2026-08-07T11:20:58.846464Z","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":"2026-08-07T11:20:58.849482Z","title":"A closer look at the cls token for cross-domain few-shot learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.849482Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:12eb505630216c4fc726a64adf30a18dd3def543c74082fe5f78e9c558e3fdcb","observation_id":"1f4701d7-5d01-4eda-8f02-eff5b76ae42a","resolution":{"observed_at":"2026-08-07T11:20:58.849482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:59.300078Z","title":null,"venue":null,"work_id":"63cb5829-e4d1-4c7e-b0c2-feaf072e1733","year":2021},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.852244Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:de4934936c22d7ea7c886a9ba95643a42db75ed902d15fd95d2d3768dd476920","observation_id":"8f3718d2-39a9-48f3-b28e-af5e4eeed244","resolution":{"observed_at":"2026-08-07T11:20:59.303664Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:20:58.855037Z","title":"Margin-based few-shot class-incremental learning with class-level overfitting mitigation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.855037Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:aad198b4bf6e991e70b1f8c2a1d662ba9a648119b71fd7389cbac180f1bcea71","observation_id":"d00705e5-7c8f-4d0f-89c6-688e4e5d4dfa","resolution":{"observed_at":"2026-08-07T11:20:58.855037Z","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":"2026-08-07T11:20:58.857785Z","title":"Flatten long-range loss landscapes for cross-domain few-shot learning, 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.857785Z"},"links":{"citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:52c01268ff879f97d6c951e66c3302cf0fb38739a0900171a79ca94aaddb758f","observation_id":"14adb136-2f23-4ca3-8525-af83d3a45c90","resolution":{"observed_at":"2026-08-07T11:20:58.857785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17022","last_updated":"2024-05-27T10:21:38Z","snapshot_observed_at":"2026-07-06T18:20:27.214667Z","submitted_at":"2024-05-27T10:21:38Z","title":"Compositional Few-Shot Class-Incremental Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17022","snapshot_observed_at":"2026-08-07T11:20:58.860481Z","title":"Compositional few-shot class-incremental learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-07T11:20:58.860481Z"},"links":{"cited_paper":"/paper/2405.17022","citing_paper":"/paper/2506.02843"},"observation_digest":"sha256:9ac911f2e04aa044939b8d261070140721e5d82f4ab0467d48d61f9ee41d1cb5","observation_id":"cc1276e1-53f3-4b78-8bac-7c7ba77282b0","resolution":{"observed_at":"2026-08-07T11:20:58.860481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.02843","last_updated":"2025-06-03T13:13:58Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T11:12:06.636352Z","submitted_at":"2025-06-03T13:13:58Z","title":"Random Registers for Cross-Domain Few-Shot Learning"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":41,"verified_exact":0,"verified_fuzzy":16},"total_outbound_references":58},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 2 inbound Pith citation observations for arXiv:2506.02843."}