{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RDGT3P2WE5CDRZDWUOFRLLS3KJ","short_pith_number":"pith:RDGT3P2W","schema_version":"1.0","canonical_sha256":"88cd3dbf56274438e476a38b15ae5b52518d1c6106b4a1e511aed985991c1eb0","source":{"kind":"arxiv","id":"1604.00427","version":1},"attestation_state":"computed","paper":{"title":"Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kristen Grauman, Yu-Chuan Su","submitted_at":"2016-04-01T22:37:28Z","abstract_excerpt":"Current approaches for activity recognition often ignore constraints on computational resources: 1) they rely on extensive feature computation to obtain rich descriptors on all frames, and 2) they assume batch-mode access to the entire test video at once. We propose a new active approach to activity recognition that prioritizes \"what to compute when\" in order to make timely predictions. The main idea is to learn a policy that dynamically schedules the sequence of features to compute on selected frames of a given test video. In contrast to traditional static feature selection, our approach cont"},"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":"1604.00427","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-04-01T22:37:28Z","cross_cats_sorted":[],"title_canon_sha256":"b07686bb1bb90a424bc478e5023dcb5ab8cb0e56d5d5acb4ebac4fc97e22ffd9","abstract_canon_sha256":"5e5e931eee94840515f1c6a4dd9dbd653e72ffbea273e8c2ca7eba9c4110b757"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:49.651951Z","signature_b64":"51mv+frRK23sNUG7YGX5mzwaIjzI0CapgMLK2NFoLxKxkWzUoA6/9aJ6RtXdVyLhqsKg09jke3A4DBKKrpeLAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88cd3dbf56274438e476a38b15ae5b52518d1c6106b4a1e511aed985991c1eb0","last_reissued_at":"2026-05-18T01:17:49.651283Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:49.651283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kristen Grauman, Yu-Chuan Su","submitted_at":"2016-04-01T22:37:28Z","abstract_excerpt":"Current approaches for activity recognition often ignore constraints on computational resources: 1) they rely on extensive feature computation to obtain rich descriptors on all frames, and 2) they assume batch-mode access to the entire test video at once. We propose a new active approach to activity recognition that prioritizes \"what to compute when\" in order to make timely predictions. The main idea is to learn a policy that dynamically schedules the sequence of features to compute on selected frames of a given test video. In contrast to traditional static feature selection, our approach cont"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.00427","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":"1604.00427","created_at":"2026-05-18T01:17:49.651382+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.00427v1","created_at":"2026-05-18T01:17:49.651382+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.00427","created_at":"2026-05-18T01:17:49.651382+00:00"},{"alias_kind":"pith_short_12","alias_value":"RDGT3P2WE5CD","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RDGT3P2WE5CDRZDW","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RDGT3P2W","created_at":"2026-05-18T12:30:41.710351+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/RDGT3P2WE5CDRZDWUOFRLLS3KJ","json":"https://pith.science/pith/RDGT3P2WE5CDRZDWUOFRLLS3KJ.json","graph_json":"https://pith.science/api/pith-number/RDGT3P2WE5CDRZDWUOFRLLS3KJ/graph.json","events_json":"https://pith.science/api/pith-number/RDGT3P2WE5CDRZDWUOFRLLS3KJ/events.json","paper":"https://pith.science/paper/RDGT3P2W"},"agent_actions":{"view_html":"https://pith.science/pith/RDGT3P2WE5CDRZDWUOFRLLS3KJ","download_json":"https://pith.science/pith/RDGT3P2WE5CDRZDWUOFRLLS3KJ.json","view_paper":"https://pith.science/paper/RDGT3P2W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.00427&json=true","fetch_graph":"https://pith.science/api/pith-number/RDGT3P2WE5CDRZDWUOFRLLS3KJ/graph.json","fetch_events":"https://pith.science/api/pith-number/RDGT3P2WE5CDRZDWUOFRLLS3KJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RDGT3P2WE5CDRZDWUOFRLLS3KJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RDGT3P2WE5CDRZDWUOFRLLS3KJ/action/storage_attestation","attest_author":"https://pith.science/pith/RDGT3P2WE5CDRZDWUOFRLLS3KJ/action/author_attestation","sign_citation":"https://pith.science/pith/RDGT3P2WE5CDRZDWUOFRLLS3KJ/action/citation_signature","submit_replication":"https://pith.science/pith/RDGT3P2WE5CDRZDWUOFRLLS3KJ/action/replication_record"}},"created_at":"2026-05-18T01:17:49.651382+00:00","updated_at":"2026-05-18T01:17:49.651382+00:00"}