{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:CIQO7M6TKIMGMNEWYP6STDAADV","short_pith_number":"pith:CIQO7M6T","schema_version":"1.0","canonical_sha256":"1220efb3d35218663496c3fd298c001d7864df4ef29e14ce4465a0cbb3b7a3b2","source":{"kind":"arxiv","id":"1505.04868","version":1},"attestation_state":"computed","paper":{"title":"Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Limin Wang, Xiaoou Tang, Yu Qiao","submitted_at":"2015-05-19T04:36:42Z","abstract_excerpt":"Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features and deep-learned features. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely"},"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":"1505.04868","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-05-19T04:36:42Z","cross_cats_sorted":[],"title_canon_sha256":"5b7cf3d4cc1de4bf31c3105cc022af2917c5243b36659b383faff0b326c9baf3","abstract_canon_sha256":"25a1ba360e59ddc1faf786a0548be91b8fa9ae2720bc5cfcbaf7bee9eb8d38be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:58:10.764533Z","signature_b64":"uWaqYTkCXihhRlp1aCXyqPjCEIj1HDGB7K0H0I9CMClNfI59WkuMI6LULeVEEFdEQCy+q5CNmwHM0mQ9ThFLCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1220efb3d35218663496c3fd298c001d7864df4ef29e14ce4465a0cbb3b7a3b2","last_reissued_at":"2026-05-18T00:58:10.763871Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:58:10.763871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Limin Wang, Xiaoou Tang, Yu Qiao","submitted_at":"2015-05-19T04:36:42Z","abstract_excerpt":"Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features and deep-learned features. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.04868","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":"1505.04868","created_at":"2026-05-18T00:58:10.764010+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.04868v1","created_at":"2026-05-18T00:58:10.764010+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.04868","created_at":"2026-05-18T00:58:10.764010+00:00"},{"alias_kind":"pith_short_12","alias_value":"CIQO7M6TKIMG","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_16","alias_value":"CIQO7M6TKIMGMNEW","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_8","alias_value":"CIQO7M6T","created_at":"2026-05-18T12:29:14.074870+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/CIQO7M6TKIMGMNEWYP6STDAADV","json":"https://pith.science/pith/CIQO7M6TKIMGMNEWYP6STDAADV.json","graph_json":"https://pith.science/api/pith-number/CIQO7M6TKIMGMNEWYP6STDAADV/graph.json","events_json":"https://pith.science/api/pith-number/CIQO7M6TKIMGMNEWYP6STDAADV/events.json","paper":"https://pith.science/paper/CIQO7M6T"},"agent_actions":{"view_html":"https://pith.science/pith/CIQO7M6TKIMGMNEWYP6STDAADV","download_json":"https://pith.science/pith/CIQO7M6TKIMGMNEWYP6STDAADV.json","view_paper":"https://pith.science/paper/CIQO7M6T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.04868&json=true","fetch_graph":"https://pith.science/api/pith-number/CIQO7M6TKIMGMNEWYP6STDAADV/graph.json","fetch_events":"https://pith.science/api/pith-number/CIQO7M6TKIMGMNEWYP6STDAADV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CIQO7M6TKIMGMNEWYP6STDAADV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CIQO7M6TKIMGMNEWYP6STDAADV/action/storage_attestation","attest_author":"https://pith.science/pith/CIQO7M6TKIMGMNEWYP6STDAADV/action/author_attestation","sign_citation":"https://pith.science/pith/CIQO7M6TKIMGMNEWYP6STDAADV/action/citation_signature","submit_replication":"https://pith.science/pith/CIQO7M6TKIMGMNEWYP6STDAADV/action/replication_record"}},"created_at":"2026-05-18T00:58:10.764010+00:00","updated_at":"2026-05-18T00:58:10.764010+00:00"}