{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7LD24D7URLH7VIOQOGK7VJUUCU","short_pith_number":"pith:7LD24D7U","schema_version":"1.0","canonical_sha256":"fac7ae0ff48acffaa1d07195faa6941517adfb005b43202496f163cc25f77ec3","source":{"kind":"arxiv","id":"2207.09373","version":3},"attestation_state":"computed","paper":{"title":"Multi-Task Learning Framework for Emotion Recognition in-the-wild","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuanhe Liu, Fengyuan Zhang, Jinming Zhao, Lei Sun, Liyu Meng, Qin Jin, Tenggan Zhang, Wenqiang Jiang, Xiaolong Liu, Yuchen Liu","submitted_at":"2022-07-19T16:18:53Z","abstract_excerpt":"This paper presents our system for the Multi-Task Learning (MTL) Challenge in the 4th Affective Behavior Analysis in-the-wild (ABAW) competition. We explore the research problems of this challenge from three aspects: 1) For obtaining efficient and robust visual feature representations, we propose MAE-based unsupervised representation learning and IResNet/DenseNet-based supervised representation learning methods; 2) Considering the importance of temporal information in videos, we explore three types of sequential encoders to capture the temporal information, including the encoder based on trans"},"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":"2207.09373","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-19T16:18:53Z","cross_cats_sorted":[],"title_canon_sha256":"0df78c83e9efa9d42d2fd7a8d45fc76653fe3cc30566a3b9866991d793c64fe1","abstract_canon_sha256":"191f7f57979415a399240df95850d5776d960e9e0d94975e6f0817950411f588"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:53:01.613847Z","signature_b64":"tsCU6ufOcKFniZa+4SQvcT9CzdbZaNPtwxTFNSMOOgFkIymJpXxb6+xHaKDXQ/uvo3bnBut0/enpc5P0HGN7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fac7ae0ff48acffaa1d07195faa6941517adfb005b43202496f163cc25f77ec3","last_reissued_at":"2026-07-05T04:53:01.613289Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:53:01.613289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Task Learning Framework for Emotion Recognition in-the-wild","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuanhe Liu, Fengyuan Zhang, Jinming Zhao, Lei Sun, Liyu Meng, Qin Jin, Tenggan Zhang, Wenqiang Jiang, Xiaolong Liu, Yuchen Liu","submitted_at":"2022-07-19T16:18:53Z","abstract_excerpt":"This paper presents our system for the Multi-Task Learning (MTL) Challenge in the 4th Affective Behavior Analysis in-the-wild (ABAW) competition. We explore the research problems of this challenge from three aspects: 1) For obtaining efficient and robust visual feature representations, we propose MAE-based unsupervised representation learning and IResNet/DenseNet-based supervised representation learning methods; 2) Considering the importance of temporal information in videos, we explore three types of sequential encoders to capture the temporal information, including the encoder based on trans"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.09373","kind":"arxiv","version":3},"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/2207.09373/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":"2207.09373","created_at":"2026-07-05T04:53:01.613353+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.09373v3","created_at":"2026-07-05T04:53:01.613353+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.09373","created_at":"2026-07-05T04:53:01.613353+00:00"},{"alias_kind":"pith_short_12","alias_value":"7LD24D7URLH7","created_at":"2026-07-05T04:53:01.613353+00:00"},{"alias_kind":"pith_short_16","alias_value":"7LD24D7URLH7VIOQ","created_at":"2026-07-05T04:53:01.613353+00:00"},{"alias_kind":"pith_short_8","alias_value":"7LD24D7U","created_at":"2026-07-05T04:53:01.613353+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/7LD24D7URLH7VIOQOGK7VJUUCU","json":"https://pith.science/pith/7LD24D7URLH7VIOQOGK7VJUUCU.json","graph_json":"https://pith.science/api/pith-number/7LD24D7URLH7VIOQOGK7VJUUCU/graph.json","events_json":"https://pith.science/api/pith-number/7LD24D7URLH7VIOQOGK7VJUUCU/events.json","paper":"https://pith.science/paper/7LD24D7U"},"agent_actions":{"view_html":"https://pith.science/pith/7LD24D7URLH7VIOQOGK7VJUUCU","download_json":"https://pith.science/pith/7LD24D7URLH7VIOQOGK7VJUUCU.json","view_paper":"https://pith.science/paper/7LD24D7U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.09373&json=true","fetch_graph":"https://pith.science/api/pith-number/7LD24D7URLH7VIOQOGK7VJUUCU/graph.json","fetch_events":"https://pith.science/api/pith-number/7LD24D7URLH7VIOQOGK7VJUUCU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7LD24D7URLH7VIOQOGK7VJUUCU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7LD24D7URLH7VIOQOGK7VJUUCU/action/storage_attestation","attest_author":"https://pith.science/pith/7LD24D7URLH7VIOQOGK7VJUUCU/action/author_attestation","sign_citation":"https://pith.science/pith/7LD24D7URLH7VIOQOGK7VJUUCU/action/citation_signature","submit_replication":"https://pith.science/pith/7LD24D7URLH7VIOQOGK7VJUUCU/action/replication_record"}},"created_at":"2026-07-05T04:53:01.613353+00:00","updated_at":"2026-07-05T04:53:01.613353+00:00"}