{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:TFKYQMS22LSSAN2DPWQGOML5SS","short_pith_number":"pith:TFKYQMS2","schema_version":"1.0","canonical_sha256":"995588325ad2e52037437da067317d94802ec0bc1247bc740f69ffc4d46c5360","source":{"kind":"arxiv","id":"1705.09467","version":1},"attestation_state":"computed","paper":{"title":"Predicting Human Interaction via Relative Attention Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingbing Ni, Xiaokang Yang, Yichao Yan","submitted_at":"2017-05-26T08:04:24Z","abstract_excerpt":"Predicting human interaction is challenging as the on-going activity has to be inferred based on a partially observed video. Essentially, a good algorithm should effectively model the mutual influence between the two interacting subjects. Also, only a small region in the scene is discriminative for identifying the on-going interaction. In this work, we propose a relative attention model to explicitly address these difficulties. Built on a tri-coupled deep recurrent structure representing both interacting subjects and global interaction status, the proposed network collects spatio-temporal info"},"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":"1705.09467","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-26T08:04:24Z","cross_cats_sorted":[],"title_canon_sha256":"7dbe73a2212aacbe30503fe017411a9eb1bdcdfe69d82d42d8c7b2ab4655afe7","abstract_canon_sha256":"76b4dd2411a924c33dc49f97cb0d6fa9114b25df05b3243e2567822302fc5bda"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:38.644271Z","signature_b64":"1QNLdxSHgRxO8T489HEwscoTqv4Kt6cpkXxgR/7OmrRkCjNzJVZutyTBKQ+SQiU/OuMyEaumh4XajoH734msDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"995588325ad2e52037437da067317d94802ec0bc1247bc740f69ffc4d46c5360","last_reissued_at":"2026-05-18T00:43:38.643756Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:38.643756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Human Interaction via Relative Attention Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingbing Ni, Xiaokang Yang, Yichao Yan","submitted_at":"2017-05-26T08:04:24Z","abstract_excerpt":"Predicting human interaction is challenging as the on-going activity has to be inferred based on a partially observed video. Essentially, a good algorithm should effectively model the mutual influence between the two interacting subjects. Also, only a small region in the scene is discriminative for identifying the on-going interaction. In this work, we propose a relative attention model to explicitly address these difficulties. Built on a tri-coupled deep recurrent structure representing both interacting subjects and global interaction status, the proposed network collects spatio-temporal info"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.09467","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":"1705.09467","created_at":"2026-05-18T00:43:38.643834+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.09467v1","created_at":"2026-05-18T00:43:38.643834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.09467","created_at":"2026-05-18T00:43:38.643834+00:00"},{"alias_kind":"pith_short_12","alias_value":"TFKYQMS22LSS","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"TFKYQMS22LSSAN2D","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"TFKYQMS2","created_at":"2026-05-18T12:31:46.661854+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/TFKYQMS22LSSAN2DPWQGOML5SS","json":"https://pith.science/pith/TFKYQMS22LSSAN2DPWQGOML5SS.json","graph_json":"https://pith.science/api/pith-number/TFKYQMS22LSSAN2DPWQGOML5SS/graph.json","events_json":"https://pith.science/api/pith-number/TFKYQMS22LSSAN2DPWQGOML5SS/events.json","paper":"https://pith.science/paper/TFKYQMS2"},"agent_actions":{"view_html":"https://pith.science/pith/TFKYQMS22LSSAN2DPWQGOML5SS","download_json":"https://pith.science/pith/TFKYQMS22LSSAN2DPWQGOML5SS.json","view_paper":"https://pith.science/paper/TFKYQMS2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.09467&json=true","fetch_graph":"https://pith.science/api/pith-number/TFKYQMS22LSSAN2DPWQGOML5SS/graph.json","fetch_events":"https://pith.science/api/pith-number/TFKYQMS22LSSAN2DPWQGOML5SS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TFKYQMS22LSSAN2DPWQGOML5SS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TFKYQMS22LSSAN2DPWQGOML5SS/action/storage_attestation","attest_author":"https://pith.science/pith/TFKYQMS22LSSAN2DPWQGOML5SS/action/author_attestation","sign_citation":"https://pith.science/pith/TFKYQMS22LSSAN2DPWQGOML5SS/action/citation_signature","submit_replication":"https://pith.science/pith/TFKYQMS22LSSAN2DPWQGOML5SS/action/replication_record"}},"created_at":"2026-05-18T00:43:38.643834+00:00","updated_at":"2026-05-18T00:43:38.643834+00:00"}