{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BF2I5RX2SMZSCTDMX4HG7BMXF3","short_pith_number":"pith:BF2I5RX2","canonical_record":{"source":{"id":"2511.10958","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T04:49:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0e18eb07cac30645958f8d604c30dc7ab64c1eab7f7893e839d33431b825a38e","abstract_canon_sha256":"ffd67bb4eb0cb9ebbf3d927f6b08101fd235200e92b80c7461cae11234289830"},"schema_version":"1.0"},"canonical_sha256":"09748ec6fa9333214c6cbf0e6f85972ef7e9ea30506eb7f98ceb96e757eb4f6f","source":{"kind":"arxiv","id":"2511.10958","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"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BF2I5RX2SMZSCTDMX4HG7BMXF3","target":"record","payload":{"canonical_record":{"source":{"id":"2511.10958","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T04:49:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0e18eb07cac30645958f8d604c30dc7ab64c1eab7f7893e839d33431b825a38e","abstract_canon_sha256":"ffd67bb4eb0cb9ebbf3d927f6b08101fd235200e92b80c7461cae11234289830"},"schema_version":"1.0"},"canonical_sha256":"09748ec6fa9333214c6cbf0e6f85972ef7e9ea30506eb7f98ceb96e757eb4f6f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:17:42.910843Z","signature_b64":"Oc4JE1Lf7JoDjT/3kSNgcHBVkhY/uDmgL2bhLT6fVvKgNZ/P96MZZMw6TYNesi/Pm4g4YQ7BKU8QSE6gLMtaDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09748ec6fa9333214c6cbf0e6f85972ef7e9ea30506eb7f98ceb96e757eb4f6f","last_reissued_at":"2026-07-01T01:17:42.910210Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:17:42.910210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2511.10958","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":"2026-07-01T01:17:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZU8vsnwwODxNHT8hsbMNDojnDFA+5XVY0z+YDev8e5mZe6zJmYPick5PeaZSWfBMWA8dpAvSKoOzu+DF0PR6Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T07:55:47.395348Z"},"content_sha256":"22564dcc824ff6310ace2f7c3dba374156d7575d8ad066b03cd4d00941252a84","schema_version":"1.0","event_id":"sha256:22564dcc824ff6310ace2f7c3dba374156d7575d8ad066b03cd4d00941252a84"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BF2I5RX2SMZSCTDMX4HG7BMXF3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Text-guided Weakly Supervised Framework for Dynamic Facial Expression Recognition","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gunho Jung, Heejo Kong, Seong-Whan Lee","submitted_at":"2025-11-14T04:49:58Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.10958","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/2511.10958/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":"2026-07-01T01:17:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bB6SLG2fpul5ZfGvIy133KK73AikLWyUJQi+vHR+P1CNCrwtBnVp0K7Y+VZt92PB/HjQeBiAIAx2arplvaNmDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T07:55:47.395732Z"},"content_sha256":"e97808f6d7731a7780ad7d079b0fad9c0e478be2707aa9713ef0ed054f9aa513","schema_version":"1.0","event_id":"sha256:e97808f6d7731a7780ad7d079b0fad9c0e478be2707aa9713ef0ed054f9aa513"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BF2I5RX2SMZSCTDMX4HG7BMXF3/bundle.json","state_url":"https://pith.science/pith/BF2I5RX2SMZSCTDMX4HG7BMXF3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BF2I5RX2SMZSCTDMX4HG7BMXF3/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":"2026-07-03T07:55:47Z","links":{"resolver":"https://pith.science/pith/BF2I5RX2SMZSCTDMX4HG7BMXF3","bundle":"https://pith.science/pith/BF2I5RX2SMZSCTDMX4HG7BMXF3/bundle.json","state":"https://pith.science/pith/BF2I5RX2SMZSCTDMX4HG7BMXF3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BF2I5RX2SMZSCTDMX4HG7BMXF3/bundle.json"},"state":{"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"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"40zO9R3CEMDj7B/0FPMCAkzqdbYgZDiqdL2Q4uX8cbKNzogxxMs5LMlao0NvcW1+t5WvqbbL26lo3u2gOXFUDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T07:55:47.397837Z","bundle_sha256":"8d55417816b34afa742d06d1f36d6023c38b894f40a12537036b96ebb95f1dea"}}