{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:ZRI4W6C7FO7R4TU3HSK7QK6VBI","short_pith_number":"pith:ZRI4W6C7","schema_version":"1.0","canonical_sha256":"cc51cb785f2bbf1e4e9b3c95f82bd50a18ac45b3a64feb867fa9b2a4fb761070","source":{"kind":"arxiv","id":"2004.07568","version":1},"attestation_state":"computed","paper":{"title":"Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fa-Ting Hong, Wei-Hong Li, Wei-Shi Zheng","submitted_at":"2020-04-16T10:09:37Z","abstract_excerpt":"Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern. However, existing methods rely heavily on supervised learning using large quantities of annotated image samples, which are more costly to collect for important people detection than for individual entity recognition (eg, object recognition). To overcome this problem, we propose learning important people detection on partially annotated images. Our approach iteratively learns to assign pseudo-labels"},"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":"2004.07568","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-04-16T10:09:37Z","cross_cats_sorted":[],"title_canon_sha256":"285e2bb9bbd7758681fcf258da37049dac16b0b3e3f3729acfb23a32694c418e","abstract_canon_sha256":"894328b0f49f921300d0f1d2140a75b198c17520848a756e3cf4e4988c5dc3aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:55:34.492184Z","signature_b64":"mQg7b++J6oRuapRhNby7xEJBzN8Ftetyy4V1l/O3X7m77y1Gzv1iSuzHUks68AT9NXnFxb2jgEJ/AKFkQmjUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc51cb785f2bbf1e4e9b3c95f82bd50a18ac45b3a64feb867fa9b2a4fb761070","last_reissued_at":"2026-07-05T00:55:34.491789Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:55:34.491789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fa-Ting Hong, Wei-Hong Li, Wei-Shi Zheng","submitted_at":"2020-04-16T10:09:37Z","abstract_excerpt":"Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern. However, existing methods rely heavily on supervised learning using large quantities of annotated image samples, which are more costly to collect for important people detection than for individual entity recognition (eg, object recognition). To overcome this problem, we propose learning important people detection on partially annotated images. Our approach iteratively learns to assign pseudo-labels"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.07568","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/2004.07568/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":"2004.07568","created_at":"2026-07-05T00:55:34.491852+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.07568v1","created_at":"2026-07-05T00:55:34.491852+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.07568","created_at":"2026-07-05T00:55:34.491852+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZRI4W6C7FO7R","created_at":"2026-07-05T00:55:34.491852+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZRI4W6C7FO7R4TU3","created_at":"2026-07-05T00:55:34.491852+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZRI4W6C7","created_at":"2026-07-05T00:55:34.491852+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/ZRI4W6C7FO7R4TU3HSK7QK6VBI","json":"https://pith.science/pith/ZRI4W6C7FO7R4TU3HSK7QK6VBI.json","graph_json":"https://pith.science/api/pith-number/ZRI4W6C7FO7R4TU3HSK7QK6VBI/graph.json","events_json":"https://pith.science/api/pith-number/ZRI4W6C7FO7R4TU3HSK7QK6VBI/events.json","paper":"https://pith.science/paper/ZRI4W6C7"},"agent_actions":{"view_html":"https://pith.science/pith/ZRI4W6C7FO7R4TU3HSK7QK6VBI","download_json":"https://pith.science/pith/ZRI4W6C7FO7R4TU3HSK7QK6VBI.json","view_paper":"https://pith.science/paper/ZRI4W6C7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.07568&json=true","fetch_graph":"https://pith.science/api/pith-number/ZRI4W6C7FO7R4TU3HSK7QK6VBI/graph.json","fetch_events":"https://pith.science/api/pith-number/ZRI4W6C7FO7R4TU3HSK7QK6VBI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZRI4W6C7FO7R4TU3HSK7QK6VBI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZRI4W6C7FO7R4TU3HSK7QK6VBI/action/storage_attestation","attest_author":"https://pith.science/pith/ZRI4W6C7FO7R4TU3HSK7QK6VBI/action/author_attestation","sign_citation":"https://pith.science/pith/ZRI4W6C7FO7R4TU3HSK7QK6VBI/action/citation_signature","submit_replication":"https://pith.science/pith/ZRI4W6C7FO7R4TU3HSK7QK6VBI/action/replication_record"}},"created_at":"2026-07-05T00:55:34.491852+00:00","updated_at":"2026-07-05T00:55:34.491852+00:00"}