{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RBNF6BN56YY747CHMPY4JW4FQV","short_pith_number":"pith:RBNF6BN5","schema_version":"1.0","canonical_sha256":"885a5f05bdf631fe7c4763f1c4db8585473690d24fe46140e7c052f5bef46734","source":{"kind":"arxiv","id":"2412.20084","version":1},"attestation_state":"computed","paper":{"title":"STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiamu Sheng, Kewei Wu, Mengyang Zhao, Tian Wang, Xinhua Zeng, Xuan Yang, Yang Liu, Yu-Gang Jiang, Zhangxun Li","submitted_at":"2024-12-28T08:49:23Z","abstract_excerpt":"Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems. However, most existing methods based on CNNs and transformers still suffer from substantial computational burdens and have room for improvement in learning spatial-temporal normality. Recently, Mamba has shown great potential for modeling long-range dependencies with linear complexity, providing an effective solution to the above dilemma. To this end, we propose a lightweight and effective Mamba-based network named STNMamba, which incorporates carefully designed Mamba modules to en"},"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":"2412.20084","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T08:49:23Z","cross_cats_sorted":[],"title_canon_sha256":"8157cf80b6eb16c12e16b6339a8374f1bee8cbb5addbc57b7ca201d7f61197b0","abstract_canon_sha256":"9d19ee6fe072c451e6849c476618b2e77bdc44b9b4361c3330e0d2995d0aeff9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:54.406497Z","signature_b64":"I8vhC5HwxVjSL8IZRuxVBi1dTDllDIH6zVYOrRYtLMrtFeemh5HoKcZcEg04PZmckRDjYGK+RDypbBEGF26XBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"885a5f05bdf631fe7c4763f1c4db8585473690d24fe46140e7c052f5bef46734","last_reissued_at":"2026-07-05T09:54:54.406083Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:54.406083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiamu Sheng, Kewei Wu, Mengyang Zhao, Tian Wang, Xinhua Zeng, Xuan Yang, Yang Liu, Yu-Gang Jiang, Zhangxun Li","submitted_at":"2024-12-28T08:49:23Z","abstract_excerpt":"Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems. However, most existing methods based on CNNs and transformers still suffer from substantial computational burdens and have room for improvement in learning spatial-temporal normality. Recently, Mamba has shown great potential for modeling long-range dependencies with linear complexity, providing an effective solution to the above dilemma. To this end, we propose a lightweight and effective Mamba-based network named STNMamba, which incorporates carefully designed Mamba modules to en"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20084","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/2412.20084/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":"2412.20084","created_at":"2026-07-05T09:54:54.406142+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.20084v1","created_at":"2026-07-05T09:54:54.406142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20084","created_at":"2026-07-05T09:54:54.406142+00:00"},{"alias_kind":"pith_short_12","alias_value":"RBNF6BN56YY7","created_at":"2026-07-05T09:54:54.406142+00:00"},{"alias_kind":"pith_short_16","alias_value":"RBNF6BN56YY747CH","created_at":"2026-07-05T09:54:54.406142+00:00"},{"alias_kind":"pith_short_8","alias_value":"RBNF6BN5","created_at":"2026-07-05T09:54:54.406142+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/RBNF6BN56YY747CHMPY4JW4FQV","json":"https://pith.science/pith/RBNF6BN56YY747CHMPY4JW4FQV.json","graph_json":"https://pith.science/api/pith-number/RBNF6BN56YY747CHMPY4JW4FQV/graph.json","events_json":"https://pith.science/api/pith-number/RBNF6BN56YY747CHMPY4JW4FQV/events.json","paper":"https://pith.science/paper/RBNF6BN5"},"agent_actions":{"view_html":"https://pith.science/pith/RBNF6BN56YY747CHMPY4JW4FQV","download_json":"https://pith.science/pith/RBNF6BN56YY747CHMPY4JW4FQV.json","view_paper":"https://pith.science/paper/RBNF6BN5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.20084&json=true","fetch_graph":"https://pith.science/api/pith-number/RBNF6BN56YY747CHMPY4JW4FQV/graph.json","fetch_events":"https://pith.science/api/pith-number/RBNF6BN56YY747CHMPY4JW4FQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RBNF6BN56YY747CHMPY4JW4FQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RBNF6BN56YY747CHMPY4JW4FQV/action/storage_attestation","attest_author":"https://pith.science/pith/RBNF6BN56YY747CHMPY4JW4FQV/action/author_attestation","sign_citation":"https://pith.science/pith/RBNF6BN56YY747CHMPY4JW4FQV/action/citation_signature","submit_replication":"https://pith.science/pith/RBNF6BN56YY747CHMPY4JW4FQV/action/replication_record"}},"created_at":"2026-07-05T09:54:54.406142+00:00","updated_at":"2026-07-05T09:54:54.406142+00:00"}