{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:P6BNYTKPU4Q67755E7YDYHSMCN","short_pith_number":"pith:P6BNYTKP","schema_version":"1.0","canonical_sha256":"7f82dc4d4fa721efffbd27f03c1e4c137606133bdc90f5465bbf12e41509e955","source":{"kind":"arxiv","id":"2301.12149","version":2},"attestation_state":"computed","paper":{"title":"POSTER++: A simpler and stronger facial expression recognition network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aibin Huang, Binling Nie, Jiawei Mao, Rui Xu, Xuesong Yin, Yuanqi Chang","submitted_at":"2023-01-28T10:23:44Z","abstract_excerpt":"Facial expression recognition (FER) plays an important role in a variety of real-world applications such as human-computer interaction. POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design. However, the architecture of POSTER is undoubtedly complex. It causes expensive computational costs. In order to relieve the computational pressure of POSTER, in this paper, we propose POSTER++. It improves POSTER in three directions: cross-fusion, two-stream, and multi-scale feature extracti"},"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":"2301.12149","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-01-28T10:23:44Z","cross_cats_sorted":[],"title_canon_sha256":"3df76575a7de2c7ace5c1837d31ac5fd1ee65c190eb71c22d73b98b8cf98101a","abstract_canon_sha256":"dd7482b044746594e2b8c1ac9ecee64690c7b5bb0b92b632c6637611ba0c0426"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:53.066566Z","signature_b64":"YQZ9pijh9kXx3cUXTB3C4kqY98C4fFBGlK8SEc9e2BxKKJtyMmJoRQDUsKvYzs1ZD5dRaQe8mGcHaAPIwKTDDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f82dc4d4fa721efffbd27f03c1e4c137606133bdc90f5465bbf12e41509e955","last_reissued_at":"2026-07-05T05:40:53.066104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:53.066104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"POSTER++: A simpler and stronger facial expression recognition network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aibin Huang, Binling Nie, Jiawei Mao, Rui Xu, Xuesong Yin, Yuanqi Chang","submitted_at":"2023-01-28T10:23:44Z","abstract_excerpt":"Facial expression recognition (FER) plays an important role in a variety of real-world applications such as human-computer interaction. POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design. However, the architecture of POSTER is undoubtedly complex. It causes expensive computational costs. In order to relieve the computational pressure of POSTER, in this paper, we propose POSTER++. It improves POSTER in three directions: cross-fusion, two-stream, and multi-scale feature extracti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.12149","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/2301.12149/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":"2301.12149","created_at":"2026-07-05T05:40:53.066160+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.12149v2","created_at":"2026-07-05T05:40:53.066160+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.12149","created_at":"2026-07-05T05:40:53.066160+00:00"},{"alias_kind":"pith_short_12","alias_value":"P6BNYTKPU4Q6","created_at":"2026-07-05T05:40:53.066160+00:00"},{"alias_kind":"pith_short_16","alias_value":"P6BNYTKPU4Q67755","created_at":"2026-07-05T05:40:53.066160+00:00"},{"alias_kind":"pith_short_8","alias_value":"P6BNYTKP","created_at":"2026-07-05T05:40:53.066160+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.15923","citing_title":"Hierarchical Codec Diffusion for Video-to-Speech Generation","ref_index":35,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN","json":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN.json","graph_json":"https://pith.science/api/pith-number/P6BNYTKPU4Q67755E7YDYHSMCN/graph.json","events_json":"https://pith.science/api/pith-number/P6BNYTKPU4Q67755E7YDYHSMCN/events.json","paper":"https://pith.science/paper/P6BNYTKP"},"agent_actions":{"view_html":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN","download_json":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN.json","view_paper":"https://pith.science/paper/P6BNYTKP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.12149&json=true","fetch_graph":"https://pith.science/api/pith-number/P6BNYTKPU4Q67755E7YDYHSMCN/graph.json","fetch_events":"https://pith.science/api/pith-number/P6BNYTKPU4Q67755E7YDYHSMCN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN/action/storage_attestation","attest_author":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN/action/author_attestation","sign_citation":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN/action/citation_signature","submit_replication":"https://pith.science/pith/P6BNYTKPU4Q67755E7YDYHSMCN/action/replication_record"}},"created_at":"2026-07-05T05:40:53.066160+00:00","updated_at":"2026-07-05T05:40:53.066160+00:00"}