{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3U4N2ILQTZRPVVTVULMNHRYSZ4","short_pith_number":"pith:3U4N2ILQ","schema_version":"1.0","canonical_sha256":"dd38dd21709e62fad675a2d8d3c712cf1b5618ac115350c2b6b6899a94990ac5","source":{"kind":"arxiv","id":"2308.09247","version":1},"attestation_state":"computed","paper":{"title":"Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gang Xiao, Hehe Fan, Longguang Wang, Xiaoxiao Sheng, Yulan Guo, Zhiqiang Shen","submitted_at":"2023-08-18T02:17:47Z","abstract_excerpt":"We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at the clip or frame level and cannot well capture fine-grained semantics. Instead of contrasting the representations of clips or frames, in this paper, we propose a unified self-supervised framework by conducting contrastive learning at the point level. Moreover, we introduce a new pretext task by achieving semantic alignment of superpoints, which further facilitates the representations to capture semantic cues at multip"},"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":"2308.09247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-18T02:17:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4b2894b2f107f7e70bc832ed1f2c39c81026f49a1fd69ca23964b999ec243996","abstract_canon_sha256":"d2e0ad076fc02f8db5a41a614134f757439ec82bc594503d28dc2724e2728782"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:42:29.674948Z","signature_b64":"I0DhBTalscsuDtMjXxyWnMF9HKMcC2Dlxl+7PULhM29JNUisC6aJA9LDSbfhOmvIMscZwdD9uJXH8Duu8HPWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd38dd21709e62fad675a2d8d3c712cf1b5618ac115350c2b6b6899a94990ac5","last_reissued_at":"2026-07-05T06:42:29.674539Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:42:29.674539Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gang Xiao, Hehe Fan, Longguang Wang, Xiaoxiao Sheng, Yulan Guo, Zhiqiang Shen","submitted_at":"2023-08-18T02:17:47Z","abstract_excerpt":"We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at the clip or frame level and cannot well capture fine-grained semantics. Instead of contrasting the representations of clips or frames, in this paper, we propose a unified self-supervised framework by conducting contrastive learning at the point level. Moreover, we introduce a new pretext task by achieving semantic alignment of superpoints, which further facilitates the representations to capture semantic cues at multip"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.09247","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/2308.09247/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":"2308.09247","created_at":"2026-07-05T06:42:29.674602+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.09247v1","created_at":"2026-07-05T06:42:29.674602+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.09247","created_at":"2026-07-05T06:42:29.674602+00:00"},{"alias_kind":"pith_short_12","alias_value":"3U4N2ILQTZRP","created_at":"2026-07-05T06:42:29.674602+00:00"},{"alias_kind":"pith_short_16","alias_value":"3U4N2ILQTZRPVVTV","created_at":"2026-07-05T06:42:29.674602+00:00"},{"alias_kind":"pith_short_8","alias_value":"3U4N2ILQ","created_at":"2026-07-05T06:42:29.674602+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/3U4N2ILQTZRPVVTVULMNHRYSZ4","json":"https://pith.science/pith/3U4N2ILQTZRPVVTVULMNHRYSZ4.json","graph_json":"https://pith.science/api/pith-number/3U4N2ILQTZRPVVTVULMNHRYSZ4/graph.json","events_json":"https://pith.science/api/pith-number/3U4N2ILQTZRPVVTVULMNHRYSZ4/events.json","paper":"https://pith.science/paper/3U4N2ILQ"},"agent_actions":{"view_html":"https://pith.science/pith/3U4N2ILQTZRPVVTVULMNHRYSZ4","download_json":"https://pith.science/pith/3U4N2ILQTZRPVVTVULMNHRYSZ4.json","view_paper":"https://pith.science/paper/3U4N2ILQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.09247&json=true","fetch_graph":"https://pith.science/api/pith-number/3U4N2ILQTZRPVVTVULMNHRYSZ4/graph.json","fetch_events":"https://pith.science/api/pith-number/3U4N2ILQTZRPVVTVULMNHRYSZ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3U4N2ILQTZRPVVTVULMNHRYSZ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3U4N2ILQTZRPVVTVULMNHRYSZ4/action/storage_attestation","attest_author":"https://pith.science/pith/3U4N2ILQTZRPVVTVULMNHRYSZ4/action/author_attestation","sign_citation":"https://pith.science/pith/3U4N2ILQTZRPVVTVULMNHRYSZ4/action/citation_signature","submit_replication":"https://pith.science/pith/3U4N2ILQTZRPVVTVULMNHRYSZ4/action/replication_record"}},"created_at":"2026-07-05T06:42:29.674602+00:00","updated_at":"2026-07-05T06:42:29.674602+00:00"}