{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OIYUX5ZNFKNLETNLRV27FMFOMQ","short_pith_number":"pith:OIYUX5ZN","schema_version":"1.0","canonical_sha256":"72314bf72d2a9ab24dab8d75f2b0ae640e6466a9e18b18e11f9fb0344f7109d8","source":{"kind":"arxiv","id":"1704.04952","version":2},"attestation_state":"computed","paper":{"title":"AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fabio Cuzzolin, Gurkirt Singh, Suman Saha","submitted_at":"2017-04-17T13:04:46Z","abstract_excerpt":"Dominant approaches to action detection can only provide sub-optimal solutions to the problem, as they rely on seeking frame-level detections, to later compose them into \"action tubes\" in a post-processing step. With this paper we radically depart from current practice, and take a first step towards the design and implementation of a deep network architecture able to classify and regress whole video subsets, so providing a truly optimal solution of the action detection problem. In this work, in particular, we propose a novel deep net framework able to regress and classify 3D region proposals s"},"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":"1704.04952","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-17T13:04:46Z","cross_cats_sorted":[],"title_canon_sha256":"bf5dd5dddfdaba73059552b9b8bf3c35fd9723c53e21af1f193e657fc247d3b7","abstract_canon_sha256":"8f75ec86b6d325a03e33673d246d0d45f2e991a678312925391386ac85a85a18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:35.753645Z","signature_b64":"KQz1+UpE6Q0PDOTe5QdJFu1Bd50dhPTwmv7z/aqkVcu5vmKPl6U/3K4GPTXM454JtjXu6DX+GYvCGH8U9Aj4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72314bf72d2a9ab24dab8d75f2b0ae640e6466a9e18b18e11f9fb0344f7109d8","last_reissued_at":"2026-05-18T00:38:35.753267Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:35.753267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fabio Cuzzolin, Gurkirt Singh, Suman Saha","submitted_at":"2017-04-17T13:04:46Z","abstract_excerpt":"Dominant approaches to action detection can only provide sub-optimal solutions to the problem, as they rely on seeking frame-level detections, to later compose them into \"action tubes\" in a post-processing step. With this paper we radically depart from current practice, and take a first step towards the design and implementation of a deep network architecture able to classify and regress whole video subsets, so providing a truly optimal solution of the action detection problem. In this work, in particular, we propose a novel deep net framework able to regress and classify 3D region proposals s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.04952","kind":"arxiv","version":2},"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":"1704.04952","created_at":"2026-05-18T00:38:35.753327+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.04952v2","created_at":"2026-05-18T00:38:35.753327+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.04952","created_at":"2026-05-18T00:38:35.753327+00:00"},{"alias_kind":"pith_short_12","alias_value":"OIYUX5ZNFKNL","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OIYUX5ZNFKNLETNL","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OIYUX5ZN","created_at":"2026-05-18T12:31:34.259226+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/OIYUX5ZNFKNLETNLRV27FMFOMQ","json":"https://pith.science/pith/OIYUX5ZNFKNLETNLRV27FMFOMQ.json","graph_json":"https://pith.science/api/pith-number/OIYUX5ZNFKNLETNLRV27FMFOMQ/graph.json","events_json":"https://pith.science/api/pith-number/OIYUX5ZNFKNLETNLRV27FMFOMQ/events.json","paper":"https://pith.science/paper/OIYUX5ZN"},"agent_actions":{"view_html":"https://pith.science/pith/OIYUX5ZNFKNLETNLRV27FMFOMQ","download_json":"https://pith.science/pith/OIYUX5ZNFKNLETNLRV27FMFOMQ.json","view_paper":"https://pith.science/paper/OIYUX5ZN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.04952&json=true","fetch_graph":"https://pith.science/api/pith-number/OIYUX5ZNFKNLETNLRV27FMFOMQ/graph.json","fetch_events":"https://pith.science/api/pith-number/OIYUX5ZNFKNLETNLRV27FMFOMQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OIYUX5ZNFKNLETNLRV27FMFOMQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OIYUX5ZNFKNLETNLRV27FMFOMQ/action/storage_attestation","attest_author":"https://pith.science/pith/OIYUX5ZNFKNLETNLRV27FMFOMQ/action/author_attestation","sign_citation":"https://pith.science/pith/OIYUX5ZNFKNLETNLRV27FMFOMQ/action/citation_signature","submit_replication":"https://pith.science/pith/OIYUX5ZNFKNLETNLRV27FMFOMQ/action/replication_record"}},"created_at":"2026-05-18T00:38:35.753327+00:00","updated_at":"2026-05-18T00:38:35.753327+00:00"}