{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:NTTOMJQ6H4UP6OCYQT7ZYSZ2GE","short_pith_number":"pith:NTTOMJQ6","schema_version":"1.0","canonical_sha256":"6ce6e6261e3f28ff385884ff9c4b3a311b1939412740e98ea9ddd4c280bb68b7","source":{"kind":"arxiv","id":"1502.03851","version":1},"attestation_state":"computed","paper":{"title":"Discovering Human Interactions in Videos with Limited Data Labeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arash Vahdat, Greg Mori, Guang-Tong Zhou, Hossein Hajimirsadeghi, Mehran Khodabandeh, Mehrsan Javan Roshtkhari, Stephen Se","submitted_at":"2015-02-12T22:38:28Z","abstract_excerpt":"We present a novel approach for discovering human interactions in videos. Activity understanding techniques usually require a large number of labeled examples, which are not available in many practical cases. Here, we focus on recovering semantically meaningful clusters of human-human and human-object interaction in an unsupervised fashion. A new iterative solution is introduced based on Maximum Margin Clustering (MMC), which also accepts user feedback to refine clusters. This is achieved by formulating the whole process as a unified constrained latent max-margin clustering problem. Extensive "},"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":"1502.03851","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-02-12T22:38:28Z","cross_cats_sorted":[],"title_canon_sha256":"409779f62f3ee48bf0b9422f2af8d853662ce22c2c90ef42c6ed4a86e55bf70e","abstract_canon_sha256":"89dbeb7dc0bc8bdb232dbbefa05b523b98ad74af060d0a693d65c0ad69a5949e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:27:07.126838Z","signature_b64":"wlAvFThCT8nIpOXxn4hINLOxuHvJiQQiH+ndjVGrf7EgXPEmJXksIm1DyKcrZfdhLEPgMtiX3EdAlwYYWpqjBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ce6e6261e3f28ff385884ff9c4b3a311b1939412740e98ea9ddd4c280bb68b7","last_reissued_at":"2026-05-18T02:27:07.126129Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:27:07.126129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Discovering Human Interactions in Videos with Limited Data Labeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arash Vahdat, Greg Mori, Guang-Tong Zhou, Hossein Hajimirsadeghi, Mehran Khodabandeh, Mehrsan Javan Roshtkhari, Stephen Se","submitted_at":"2015-02-12T22:38:28Z","abstract_excerpt":"We present a novel approach for discovering human interactions in videos. Activity understanding techniques usually require a large number of labeled examples, which are not available in many practical cases. Here, we focus on recovering semantically meaningful clusters of human-human and human-object interaction in an unsupervised fashion. A new iterative solution is introduced based on Maximum Margin Clustering (MMC), which also accepts user feedback to refine clusters. This is achieved by formulating the whole process as a unified constrained latent max-margin clustering problem. Extensive "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.03851","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":""},"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":"1502.03851","created_at":"2026-05-18T02:27:07.126263+00:00"},{"alias_kind":"arxiv_version","alias_value":"1502.03851v1","created_at":"2026-05-18T02:27:07.126263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.03851","created_at":"2026-05-18T02:27:07.126263+00:00"},{"alias_kind":"pith_short_12","alias_value":"NTTOMJQ6H4UP","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_16","alias_value":"NTTOMJQ6H4UP6OCY","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_8","alias_value":"NTTOMJQ6","created_at":"2026-05-18T12:29:34.919912+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/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE","json":"https://pith.science/pith/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE.json","graph_json":"https://pith.science/api/pith-number/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/graph.json","events_json":"https://pith.science/api/pith-number/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/events.json","paper":"https://pith.science/paper/NTTOMJQ6"},"agent_actions":{"view_html":"https://pith.science/pith/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE","download_json":"https://pith.science/pith/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE.json","view_paper":"https://pith.science/paper/NTTOMJQ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1502.03851&json=true","fetch_graph":"https://pith.science/api/pith-number/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/graph.json","fetch_events":"https://pith.science/api/pith-number/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/action/storage_attestation","attest_author":"https://pith.science/pith/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/action/author_attestation","sign_citation":"https://pith.science/pith/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/action/citation_signature","submit_replication":"https://pith.science/pith/NTTOMJQ6H4UP6OCYQT7ZYSZ2GE/action/replication_record"}},"created_at":"2026-05-18T02:27:07.126263+00:00","updated_at":"2026-05-18T02:27:07.126263+00:00"}