{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WRKHPIDGE4IYPCSAPWISSEVPEY","short_pith_number":"pith:WRKHPIDG","schema_version":"1.0","canonical_sha256":"b45477a0662711878a407d912912af26243d77c64329e0cdaec61dccd3096acd","source":{"kind":"arxiv","id":"2412.13577","version":1},"attestation_state":"computed","paper":{"title":"Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hongxun Yao, Jiankun Zhu, Jing Jiang, Pengfei Xu, Sicheng Zhao, Tingting Han, Wenbo Tang, Zhaopan Xu","submitted_at":"2024-12-18T07:51:35Z","abstract_excerpt":"Visual emotion recognition (VER), which aims at understanding humans' emotional reactions toward different visual stimuli, has attracted increasing attention. Given the subjective and ambiguous characteristics of emotion, annotating a reliable large-scale dataset is hard. For reducing reliance on data labeling, domain adaptation offers an alternative solution by adapting models trained on labeled source data to unlabeled target data. Conventional domain adaptation methods require access to source data. However, due to privacy concerns, source emotional data may be inaccessible. To address this"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2412.13577","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-18T07:51:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"26e12497a9950e3783e49a7ed200dfb0ab2d431f1d3e3cfb061164db19b6912e","abstract_canon_sha256":"9fe54db1a1fef499d473c531bf3c33beedb21d7b38a4a1296867124fae3665cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:04.283335Z","signature_b64":"4uXQmOcjCoGqWP3fS+P1dQ3s6yWaujK9A3eQhw4vjz4GsQkn8nUTO0dfUJNPnTumFC5nFjSLbLPItRZEIRcYDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b45477a0662711878a407d912912af26243d77c64329e0cdaec61dccd3096acd","last_reissued_at":"2026-07-05T09:51:04.282812Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:04.282812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hongxun Yao, Jiankun Zhu, Jing Jiang, Pengfei Xu, Sicheng Zhao, Tingting Han, Wenbo Tang, Zhaopan Xu","submitted_at":"2024-12-18T07:51:35Z","abstract_excerpt":"Visual emotion recognition (VER), which aims at understanding humans' emotional reactions toward different visual stimuli, has attracted increasing attention. Given the subjective and ambiguous characteristics of emotion, annotating a reliable large-scale dataset is hard. For reducing reliance on data labeling, domain adaptation offers an alternative solution by adapting models trained on labeled source data to unlabeled target data. Conventional domain adaptation methods require access to source data. However, due to privacy concerns, source emotional data may be inaccessible. To address this"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.13577","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/2412.13577/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2412.13577","created_at":"2026-07-05T09:51:04.282872+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.13577v1","created_at":"2026-07-05T09:51:04.282872+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.13577","created_at":"2026-07-05T09:51:04.282872+00:00"},{"alias_kind":"pith_short_12","alias_value":"WRKHPIDGE4IY","created_at":"2026-07-05T09:51:04.282872+00:00"},{"alias_kind":"pith_short_16","alias_value":"WRKHPIDGE4IYPCSA","created_at":"2026-07-05T09:51:04.282872+00:00"},{"alias_kind":"pith_short_8","alias_value":"WRKHPIDG","created_at":"2026-07-05T09:51:04.282872+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY","json":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY.json","graph_json":"https://pith.science/api/pith-number/WRKHPIDGE4IYPCSAPWISSEVPEY/graph.json","events_json":"https://pith.science/api/pith-number/WRKHPIDGE4IYPCSAPWISSEVPEY/events.json","paper":"https://pith.science/paper/WRKHPIDG"},"agent_actions":{"view_html":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY","download_json":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY.json","view_paper":"https://pith.science/paper/WRKHPIDG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.13577&json=true","fetch_graph":"https://pith.science/api/pith-number/WRKHPIDGE4IYPCSAPWISSEVPEY/graph.json","fetch_events":"https://pith.science/api/pith-number/WRKHPIDGE4IYPCSAPWISSEVPEY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY/action/storage_attestation","attest_author":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY/action/author_attestation","sign_citation":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY/action/citation_signature","submit_replication":"https://pith.science/pith/WRKHPIDGE4IYPCSAPWISSEVPEY/action/replication_record"}},"created_at":"2026-07-05T09:51:04.282872+00:00","updated_at":"2026-07-05T09:51:04.282872+00:00"}