{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GMDGPZLGGLHCBNTKZ6XXJ7TTBL","short_pith_number":"pith:GMDGPZLG","canonical_record":{"source":{"id":"2504.18782","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-26T03:26:30Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"a738d1d5f9ecb62189ca0ce25c5a5ef7c808dcaef09c88b60cacf1656610617f","abstract_canon_sha256":"6404d688e51cf3616e20a12f87dbe3fd08e9f79dae45a863394ff5f10ebc65da"},"schema_version":"1.0"},"canonical_sha256":"330667e56632ce20b66acfaf74fe730ae42f1d0126bfbd55c6db10e87b63a9aa","source":{"kind":"arxiv","id":"2504.18782","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.18782","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"arxiv_version","alias_value":"2504.18782v1","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18782","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"pith_short_12","alias_value":"GMDGPZLGGLHC","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"pith_short_16","alias_value":"GMDGPZLGGLHCBNTK","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"pith_short_8","alias_value":"GMDGPZLG","created_at":"2026-07-05T10:54:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GMDGPZLGGLHCBNTKZ6XXJ7TTBL","target":"record","payload":{"canonical_record":{"source":{"id":"2504.18782","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-26T03:26:30Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"a738d1d5f9ecb62189ca0ce25c5a5ef7c808dcaef09c88b60cacf1656610617f","abstract_canon_sha256":"6404d688e51cf3616e20a12f87dbe3fd08e9f79dae45a863394ff5f10ebc65da"},"schema_version":"1.0"},"canonical_sha256":"330667e56632ce20b66acfaf74fe730ae42f1d0126bfbd55c6db10e87b63a9aa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:21.345655Z","signature_b64":"VlmDaN569W5LOz+oc36Vz2JEfzmz+Lyz1ozFBtVltOA6eyd4BZW4JhBRmxmqs+bPgeuFVMQk9IKXwbafSI0SAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"330667e56632ce20b66acfaf74fe730ae42f1d0126bfbd55c6db10e87b63a9aa","last_reissued_at":"2026-07-05T10:54:21.345141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:21.345141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.18782","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:54:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gOKICdy0Dpx5zT6E4pN042ivm0RdUzhy2hN/tf5ZYjxavEB+nNnBX/cfY29+pPB9MbamOupGAR4Pfygym7p9Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T01:57:51.763946Z"},"content_sha256":"ae76a37d26da625e2861893e1cb167b040c54f25ab9beb829fe0dd4b6691a55f","schema_version":"1.0","event_id":"sha256:ae76a37d26da625e2861893e1cb167b040c54f25ab9beb829fe0dd4b6691a55f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GMDGPZLGGLHCBNTKZ6XXJ7TTBL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person Retrieval","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Hang Yu, Jiahao Wen, Zhedong Zheng","submitted_at":"2025-04-26T03:26:30Z","abstract_excerpt":"Text-based person retrieval aims to identify specific individuals within an image database using textual descriptions. Due to the high cost of annotation and privacy protection, researchers resort to synthesized data for the paradigm of pretraining and fine-tuning. However, these generated data often exhibit domain biases in both images and textual annotations, which largely compromise the scalability of the pre-trained model. Therefore, we introduce a domain-agnostic pretraining framework based on Cross-modality Adaptive Meta-Learning (CAMeL) to enhance the model generalization capability dur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18782","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.18782/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:54:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lHOv4m0qmeWcvn4tvKmzBGFqALI3Rcy2MhcVs6RX/IvJxGuA0ggOgq0j2T0ZRY/OuYkdupCOJG2eY/nu1UchAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T01:57:51.764582Z"},"content_sha256":"6455b6a791cd3cfbbc9a1a372d6265249690417172102e059fea57f4312d938c","schema_version":"1.0","event_id":"sha256:6455b6a791cd3cfbbc9a1a372d6265249690417172102e059fea57f4312d938c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GMDGPZLGGLHCBNTKZ6XXJ7TTBL/bundle.json","state_url":"https://pith.science/pith/GMDGPZLGGLHCBNTKZ6XXJ7TTBL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GMDGPZLGGLHCBNTKZ6XXJ7TTBL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-17T01:57:51Z","links":{"resolver":"https://pith.science/pith/GMDGPZLGGLHCBNTKZ6XXJ7TTBL","bundle":"https://pith.science/pith/GMDGPZLGGLHCBNTKZ6XXJ7TTBL/bundle.json","state":"https://pith.science/pith/GMDGPZLGGLHCBNTKZ6XXJ7TTBL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GMDGPZLGGLHCBNTKZ6XXJ7TTBL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GMDGPZLGGLHCBNTKZ6XXJ7TTBL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6404d688e51cf3616e20a12f87dbe3fd08e9f79dae45a863394ff5f10ebc65da","cross_cats_sorted":["cs.MM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-26T03:26:30Z","title_canon_sha256":"a738d1d5f9ecb62189ca0ce25c5a5ef7c808dcaef09c88b60cacf1656610617f"},"schema_version":"1.0","source":{"id":"2504.18782","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.18782","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"arxiv_version","alias_value":"2504.18782v1","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18782","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"pith_short_12","alias_value":"GMDGPZLGGLHC","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"pith_short_16","alias_value":"GMDGPZLGGLHCBNTK","created_at":"2026-07-05T10:54:21Z"},{"alias_kind":"pith_short_8","alias_value":"GMDGPZLG","created_at":"2026-07-05T10:54:21Z"}],"graph_snapshots":[{"event_id":"sha256:6455b6a791cd3cfbbc9a1a372d6265249690417172102e059fea57f4312d938c","target":"graph","created_at":"2026-07-05T10:54:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.18782/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-based person retrieval aims to identify specific individuals within an image database using textual descriptions. Due to the high cost of annotation and privacy protection, researchers resort to synthesized data for the paradigm of pretraining and fine-tuning. However, these generated data often exhibit domain biases in both images and textual annotations, which largely compromise the scalability of the pre-trained model. Therefore, we introduce a domain-agnostic pretraining framework based on Cross-modality Adaptive Meta-Learning (CAMeL) to enhance the model generalization capability dur","authors_text":"Hang Yu, Jiahao Wen, Zhedong Zheng","cross_cats":["cs.MM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-26T03:26:30Z","title":"CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18782","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ae76a37d26da625e2861893e1cb167b040c54f25ab9beb829fe0dd4b6691a55f","target":"record","created_at":"2026-07-05T10:54:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6404d688e51cf3616e20a12f87dbe3fd08e9f79dae45a863394ff5f10ebc65da","cross_cats_sorted":["cs.MM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-26T03:26:30Z","title_canon_sha256":"a738d1d5f9ecb62189ca0ce25c5a5ef7c808dcaef09c88b60cacf1656610617f"},"schema_version":"1.0","source":{"id":"2504.18782","kind":"arxiv","version":1}},"canonical_sha256":"330667e56632ce20b66acfaf74fe730ae42f1d0126bfbd55c6db10e87b63a9aa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"330667e56632ce20b66acfaf74fe730ae42f1d0126bfbd55c6db10e87b63a9aa","first_computed_at":"2026-07-05T10:54:21.345141Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:54:21.345141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VlmDaN569W5LOz+oc36Vz2JEfzmz+Lyz1ozFBtVltOA6eyd4BZW4JhBRmxmqs+bPgeuFVMQk9IKXwbafSI0SAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:54:21.345655Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.18782","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae76a37d26da625e2861893e1cb167b040c54f25ab9beb829fe0dd4b6691a55f","sha256:6455b6a791cd3cfbbc9a1a372d6265249690417172102e059fea57f4312d938c"],"state_sha256":"14f462c2e9a4a5033a2ca2d98cb8eb21a53c20b5202faaa519c6bf82216ea9f4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O39hwuEDe0JGGTkpKpXWCrVdGmKYlbXFnLg+eI1v90RMVitj+/UdXh/zBsk5aIKeTmUoRXsUC4lmO9zGFgOiCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T01:57:51.770998Z","bundle_sha256":"887ba86129b0642ceeb45644a8400d9253819cc03b1f1b0eff7ac47cf70c8ec4"}}