{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:R3NWKMY7K6ET4Y6Q74DHTFBMKI","short_pith_number":"pith:R3NWKMY7","schema_version":"1.0","canonical_sha256":"8edb65331f57893e63d0ff0679942c5213c71716cf6d90d25942a994e0e67098","source":{"kind":"arxiv","id":"2104.03100","version":2},"attestation_state":"computed","paper":{"title":"HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian Cheng, Jian Xue, Qiang Chen, Qinghao Hu, Xing Lan","submitted_at":"2021-04-07T12:53:37Z","abstract_excerpt":"Heatmap-based regression overcomes the lack of spatial and contextual information of direct coordinate regression, and has revolutionized the task of face alignment. Yet it suffers from quantization errors caused by neglecting subpixel coordinates in image resizing and network downsampling. In this paper, we first quantitatively analyze the quantization error on benchmarks, which accounts for more than 1/3 of the whole prediction errors for state-of-the-art methods. To tackle this problem, we propose a novel Heatmap In Heatmap(HIH) representation and a coordinate soft-classification (CSC) meth"},"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":"2104.03100","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-04-07T12:53:37Z","cross_cats_sorted":[],"title_canon_sha256":"7c438fe38784d13cb2f83662bfccd36ce084c5dfb4d149ac8bc5f82ef7f6375d","abstract_canon_sha256":"07aa85f0b8533ac6900f1eecd114d390462929ff334b2cebeda8f50f7f504639"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:15:08.562487Z","signature_b64":"QkTUzg1Ws43MqH7tpbHrSUDvM8FhS+O0AbEOM2X4Y5xuZa9iEGvCkRfeAavrjK3sgNl/7sH6OFoBcQYEBAbJAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8edb65331f57893e63d0ff0679942c5213c71716cf6d90d25942a994e0e67098","last_reissued_at":"2026-07-05T04:15:08.562006Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:15:08.562006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian Cheng, Jian Xue, Qiang Chen, Qinghao Hu, Xing Lan","submitted_at":"2021-04-07T12:53:37Z","abstract_excerpt":"Heatmap-based regression overcomes the lack of spatial and contextual information of direct coordinate regression, and has revolutionized the task of face alignment. Yet it suffers from quantization errors caused by neglecting subpixel coordinates in image resizing and network downsampling. In this paper, we first quantitatively analyze the quantization error on benchmarks, which accounts for more than 1/3 of the whole prediction errors for state-of-the-art methods. To tackle this problem, we propose a novel Heatmap In Heatmap(HIH) representation and a coordinate soft-classification (CSC) meth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.03100","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/2104.03100/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":"2104.03100","created_at":"2026-07-05T04:15:08.562064+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.03100v2","created_at":"2026-07-05T04:15:08.562064+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.03100","created_at":"2026-07-05T04:15:08.562064+00:00"},{"alias_kind":"pith_short_12","alias_value":"R3NWKMY7K6ET","created_at":"2026-07-05T04:15:08.562064+00:00"},{"alias_kind":"pith_short_16","alias_value":"R3NWKMY7K6ET4Y6Q","created_at":"2026-07-05T04:15:08.562064+00:00"},{"alias_kind":"pith_short_8","alias_value":"R3NWKMY7","created_at":"2026-07-05T04:15:08.562064+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.11743","citing_title":"WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI","json":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI.json","graph_json":"https://pith.science/api/pith-number/R3NWKMY7K6ET4Y6Q74DHTFBMKI/graph.json","events_json":"https://pith.science/api/pith-number/R3NWKMY7K6ET4Y6Q74DHTFBMKI/events.json","paper":"https://pith.science/paper/R3NWKMY7"},"agent_actions":{"view_html":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI","download_json":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI.json","view_paper":"https://pith.science/paper/R3NWKMY7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.03100&json=true","fetch_graph":"https://pith.science/api/pith-number/R3NWKMY7K6ET4Y6Q74DHTFBMKI/graph.json","fetch_events":"https://pith.science/api/pith-number/R3NWKMY7K6ET4Y6Q74DHTFBMKI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI/action/storage_attestation","attest_author":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI/action/author_attestation","sign_citation":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI/action/citation_signature","submit_replication":"https://pith.science/pith/R3NWKMY7K6ET4Y6Q74DHTFBMKI/action/replication_record"}},"created_at":"2026-07-05T04:15:08.562064+00:00","updated_at":"2026-07-05T04:15:08.562064+00:00"}