{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ED6DGCELE3O63LWBOG3Z4WET2D","short_pith_number":"pith:ED6DGCEL","schema_version":"1.0","canonical_sha256":"20fc33088b26ddedaec171b79e5893d0e3df22dbfad3e3154e60e751d550d999","source":{"kind":"arxiv","id":"2507.09512","version":2},"attestation_state":"computed","paper":{"title":"Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dan Guo, Fei Wang, Junhui She, Kun Li, Pengyu Liu, Yanyan Wei","submitted_at":"2025-07-13T06:38:17Z","abstract_excerpt":"In this paper, we introduce the latest solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track of the IJCAI 2025 MiGA Challenge. The Micro-gesture Online Recognition task is a highly challenging problem that aims to locate the temporal positions and recognize the categories of multiple micro-gesture instances in untrimmed videos. Compared to traditional temporal action detection, this task places greater emphasis on distinguishing between micro-gesture categories and precisely identifying the start and end times of each instance. Moreover, micro-gestures are ty"},"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":"2507.09512","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-13T06:38:17Z","cross_cats_sorted":[],"title_canon_sha256":"55105e89f2b062fb85343ad0ac03159ee8b3cc60edee4910e7742c7a88600d0c","abstract_canon_sha256":"7a052c441c15a3ec3ad31786ea234b8993f1afb2a675b57b3cc44f80ee49dfea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:17.716390Z","signature_b64":"YAoUVjwAe0sLOOmDv4YkNNZEqKo+TSY45SvsDgnofyMfaJTuPEIKfoNHkiRkTTLnoXDARfJg4N75Dj/XCOkHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"20fc33088b26ddedaec171b79e5893d0e3df22dbfad3e3154e60e751d550d999","last_reissued_at":"2026-07-05T11:59:17.715880Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:17.715880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dan Guo, Fei Wang, Junhui She, Kun Li, Pengyu Liu, Yanyan Wei","submitted_at":"2025-07-13T06:38:17Z","abstract_excerpt":"In this paper, we introduce the latest solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track of the IJCAI 2025 MiGA Challenge. The Micro-gesture Online Recognition task is a highly challenging problem that aims to locate the temporal positions and recognize the categories of multiple micro-gesture instances in untrimmed videos. Compared to traditional temporal action detection, this task places greater emphasis on distinguishing between micro-gesture categories and precisely identifying the start and end times of each instance. Moreover, micro-gestures are ty"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09512","kind":"arxiv","version":2},"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/2507.09512/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":"2507.09512","created_at":"2026-07-05T11:59:17.715940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.09512v2","created_at":"2026-07-05T11:59:17.715940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09512","created_at":"2026-07-05T11:59:17.715940+00:00"},{"alias_kind":"pith_short_12","alias_value":"ED6DGCELE3O6","created_at":"2026-07-05T11:59:17.715940+00:00"},{"alias_kind":"pith_short_16","alias_value":"ED6DGCELE3O63LWB","created_at":"2026-07-05T11:59:17.715940+00:00"},{"alias_kind":"pith_short_8","alias_value":"ED6DGCEL","created_at":"2026-07-05T11:59:17.715940+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.13030","citing_title":"A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11645","citing_title":"Motion Reinforces Appearance: RGB-Skeleton Gated Residual Fusion for Micro-Gesture Online Recognition","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09261","citing_title":"Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07355","citing_title":"Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31249","citing_title":"Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D","json":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D.json","graph_json":"https://pith.science/api/pith-number/ED6DGCELE3O63LWBOG3Z4WET2D/graph.json","events_json":"https://pith.science/api/pith-number/ED6DGCELE3O63LWBOG3Z4WET2D/events.json","paper":"https://pith.science/paper/ED6DGCEL"},"agent_actions":{"view_html":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D","download_json":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D.json","view_paper":"https://pith.science/paper/ED6DGCEL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.09512&json=true","fetch_graph":"https://pith.science/api/pith-number/ED6DGCELE3O63LWBOG3Z4WET2D/graph.json","fetch_events":"https://pith.science/api/pith-number/ED6DGCELE3O63LWBOG3Z4WET2D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D/action/storage_attestation","attest_author":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D/action/author_attestation","sign_citation":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D/action/citation_signature","submit_replication":"https://pith.science/pith/ED6DGCELE3O63LWBOG3Z4WET2D/action/replication_record"}},"created_at":"2026-07-05T11:59:17.715940+00:00","updated_at":"2026-07-05T11:59:17.715940+00:00"}