{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5CASGN2CDYOXP2AF6TXSOERDAM","short_pith_number":"pith:5CASGN2C","schema_version":"1.0","canonical_sha256":"e8812337421e1d77e805f4ef27122303099a8b6e3ed25c4689510683414ab501","source":{"kind":"arxiv","id":"2502.07277","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Amir Hosein Fadaei, Mohammad-Reza A. Dehaqani","submitted_at":"2025-02-11T05:44:50Z","abstract_excerpt":"It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video content. It's a trend going to continue as video continues to dominate the digital landscape. These algorithms will extract and classify related features from the video and will use them to describe the events and objects in the video. Deep neural networks have displayed encouraging outcomes in the realm of feature extraction and video description. This paper will explore the spatiotemporal features found in videos and "},"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":"2502.07277","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-11T05:44:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b4521021a3749318cf20388b4f1e350e150dd13d495e68cf85be281f80b9c528","abstract_canon_sha256":"f463e3a68f716210b8c176f6430036e0827ff966f1675991c42eefe8a9becc2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:12:35.186195Z","signature_b64":"Vg8hDSGI5sIEKqxV/A0mq56FfMWxpMHraSFAIPFZqWiSGGY22Or0D042ORf7WzS/ST9W5pkvvaPi5bxFR9EUDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8812337421e1d77e805f4ef27122303099a8b6e3ed25c4689510683414ab501","last_reissued_at":"2026-07-05T10:12:35.185722Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:12:35.185722Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Amir Hosein Fadaei, Mohammad-Reza A. Dehaqani","submitted_at":"2025-02-11T05:44:50Z","abstract_excerpt":"It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video content. It's a trend going to continue as video continues to dominate the digital landscape. These algorithms will extract and classify related features from the video and will use them to describe the events and objects in the video. Deep neural networks have displayed encouraging outcomes in the realm of feature extraction and video description. This paper will explore the spatiotemporal features found in videos and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.07277","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.07277/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2502.07277","created_at":"2026-07-05T10:12:35.185778+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.07277v1","created_at":"2026-07-05T10:12:35.185778+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.07277","created_at":"2026-07-05T10:12:35.185778+00:00"},{"alias_kind":"pith_short_12","alias_value":"5CASGN2CDYOX","created_at":"2026-07-05T10:12:35.185778+00:00"},{"alias_kind":"pith_short_16","alias_value":"5CASGN2CDYOXP2AF","created_at":"2026-07-05T10:12:35.185778+00:00"},{"alias_kind":"pith_short_8","alias_value":"5CASGN2C","created_at":"2026-07-05T10:12:35.185778+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/5CASGN2CDYOXP2AF6TXSOERDAM","json":"https://pith.science/pith/5CASGN2CDYOXP2AF6TXSOERDAM.json","graph_json":"https://pith.science/api/pith-number/5CASGN2CDYOXP2AF6TXSOERDAM/graph.json","events_json":"https://pith.science/api/pith-number/5CASGN2CDYOXP2AF6TXSOERDAM/events.json","paper":"https://pith.science/paper/5CASGN2C"},"agent_actions":{"view_html":"https://pith.science/pith/5CASGN2CDYOXP2AF6TXSOERDAM","download_json":"https://pith.science/pith/5CASGN2CDYOXP2AF6TXSOERDAM.json","view_paper":"https://pith.science/paper/5CASGN2C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.07277&json=true","fetch_graph":"https://pith.science/api/pith-number/5CASGN2CDYOXP2AF6TXSOERDAM/graph.json","fetch_events":"https://pith.science/api/pith-number/5CASGN2CDYOXP2AF6TXSOERDAM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5CASGN2CDYOXP2AF6TXSOERDAM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5CASGN2CDYOXP2AF6TXSOERDAM/action/storage_attestation","attest_author":"https://pith.science/pith/5CASGN2CDYOXP2AF6TXSOERDAM/action/author_attestation","sign_citation":"https://pith.science/pith/5CASGN2CDYOXP2AF6TXSOERDAM/action/citation_signature","submit_replication":"https://pith.science/pith/5CASGN2CDYOXP2AF6TXSOERDAM/action/replication_record"}},"created_at":"2026-07-05T10:12:35.185778+00:00","updated_at":"2026-07-05T10:12:35.185778+00:00"}