{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BF2I5RX2SMZSCTDMX4HG7BMXF3","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":"ffd67bb4eb0cb9ebbf3d927f6b08101fd235200e92b80c7461cae11234289830","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T04:49:58Z","title_canon_sha256":"0e18eb07cac30645958f8d604c30dc7ab64c1eab7f7893e839d33431b825a38e"},"schema_version":"1.0","source":{"id":"2511.10958","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.10958","created_at":"2026-07-01T01:17:42Z"},{"alias_kind":"arxiv_version","alias_value":"2511.10958v1","created_at":"2026-07-01T01:17:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.10958","created_at":"2026-07-01T01:17:42Z"},{"alias_kind":"pith_short_12","alias_value":"BF2I5RX2SMZS","created_at":"2026-07-01T01:17:42Z"},{"alias_kind":"pith_short_16","alias_value":"BF2I5RX2SMZSCTDM","created_at":"2026-07-01T01:17:42Z"},{"alias_kind":"pith_short_8","alias_value":"BF2I5RX2","created_at":"2026-07-01T01:17:42Z"}],"graph_snapshots":[{"event_id":"sha256:e97808f6d7731a7780ad7d079b0fad9c0e478be2707aa9713ef0ed054f9aa513","target":"graph","created_at":"2026-07-01T01:17:42Z","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/2511.10958/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dynamic facial expression recognition (DFER) aims to identify emotional states by modeling the temporal changes in facial movements across video sequences. A key challenge in DFER is the many-to-one labeling problem, where a video composed of numerous frames is assigned a single emotion label. A common strategy to mitigate this issue is to formulate DFER as a Multiple Instance Learning (MIL) problem. However, MIL-based approaches inherently suffer from the visual diversity of emotional expressions and the complexity of temporal dynamics. To address this challenge, we propose TG-DFER, a text-gu","authors_text":"Gunho Jung, Heejo Kong, Seong-Whan Lee","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T04:49:58Z","title":"Text-guided Weakly Supervised Framework for Dynamic Facial Expression Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.10958","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":"sha256:22564dcc824ff6310ace2f7c3dba374156d7575d8ad066b03cd4d00941252a84","target":"record","created_at":"2026-07-01T01:17:42Z","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":"ffd67bb4eb0cb9ebbf3d927f6b08101fd235200e92b80c7461cae11234289830","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T04:49:58Z","title_canon_sha256":"0e18eb07cac30645958f8d604c30dc7ab64c1eab7f7893e839d33431b825a38e"},"schema_version":"1.0","source":{"id":"2511.10958","kind":"arxiv","version":1}},"canonical_sha256":"09748ec6fa9333214c6cbf0e6f85972ef7e9ea30506eb7f98ceb96e757eb4f6f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"09748ec6fa9333214c6cbf0e6f85972ef7e9ea30506eb7f98ceb96e757eb4f6f","first_computed_at":"2026-07-01T01:17:42.910210Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-01T01:17:42.910210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Oc4JE1Lf7JoDjT/3kSNgcHBVkhY/uDmgL2bhLT6fVvKgNZ/P96MZZMw6TYNesi/Pm4g4YQ7BKU8QSE6gLMtaDg==","signature_status":"signed_v1","signed_at":"2026-07-01T01:17:42.910843Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.10958","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:22564dcc824ff6310ace2f7c3dba374156d7575d8ad066b03cd4d00941252a84","sha256:e97808f6d7731a7780ad7d079b0fad9c0e478be2707aa9713ef0ed054f9aa513"],"state_sha256":"ccaf7441ca5e059de87c30352a1977bce2b9e337ec1fddf6d1582840655b442f"}