{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:T2OB54WTQDPY5PD2L3YINP6VK5","short_pith_number":"pith:T2OB54WT","schema_version":"1.0","canonical_sha256":"9e9c1ef2d380df8ebc7a5ef086bfd5576a553313dea3c46833a0dd003a8048a9","source":{"kind":"arxiv","id":"2110.06513","version":2},"attestation_state":"computed","paper":{"title":"Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alex Kot, Chenyu Yi, Haoliang Li, Siyuan Yang, Yap-Peng Tan","submitted_at":"2021-10-13T05:59:39Z","abstract_excerpt":"The state-of-the-art deep neural networks are vulnerable to common corruptions (e.g., input data degradations, distortions, and disturbances caused by weather changes, system error, and processing). While much progress has been made in analyzing and improving the robustness of models in image understanding, the robustness in video understanding is largely unexplored. In this paper, we establish a corruption robustness benchmark, Mini Kinetics-C and Mini SSV2-C, which considers temporal corruptions beyond spatial corruptions in images. We make the first attempt to conduct an exhaustive study on"},"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":"2110.06513","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-10-13T05:59:39Z","cross_cats_sorted":[],"title_canon_sha256":"9c45d3837c9684224eb302de1d462ab27d622b1446cf3a1857784c763123f4a9","abstract_canon_sha256":"a3c00a44db38cb21764776254ab80de9691be421b8cebdf5952a098a972ad05e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:50:12.345208Z","signature_b64":"vMKGcwnVafoSt9E23nYKNQ6KeIR+xj/hRmc6kPwsAIrHGzfKpqulKpbH1k01QuZ/TxFk4VZdz6QRwDIlENo3AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e9c1ef2d380df8ebc7a5ef086bfd5576a553313dea3c46833a0dd003a8048a9","last_reissued_at":"2026-07-05T04:50:12.344808Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:50:12.344808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alex Kot, Chenyu Yi, Haoliang Li, Siyuan Yang, Yap-Peng Tan","submitted_at":"2021-10-13T05:59:39Z","abstract_excerpt":"The state-of-the-art deep neural networks are vulnerable to common corruptions (e.g., input data degradations, distortions, and disturbances caused by weather changes, system error, and processing). While much progress has been made in analyzing and improving the robustness of models in image understanding, the robustness in video understanding is largely unexplored. In this paper, we establish a corruption robustness benchmark, Mini Kinetics-C and Mini SSV2-C, which considers temporal corruptions beyond spatial corruptions in images. We make the first attempt to conduct an exhaustive study on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.06513","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2110.06513/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":"2110.06513","created_at":"2026-07-05T04:50:12.344866+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.06513v2","created_at":"2026-07-05T04:50:12.344866+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.06513","created_at":"2026-07-05T04:50:12.344866+00:00"},{"alias_kind":"pith_short_12","alias_value":"T2OB54WTQDPY","created_at":"2026-07-05T04:50:12.344866+00:00"},{"alias_kind":"pith_short_16","alias_value":"T2OB54WTQDPY5PD2","created_at":"2026-07-05T04:50:12.344866+00:00"},{"alias_kind":"pith_short_8","alias_value":"T2OB54WT","created_at":"2026-07-05T04:50:12.344866+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07398","citing_title":"Exposing and Mitigating Temporal Attack in Deepfake Video Detection","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5","json":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5.json","graph_json":"https://pith.science/api/pith-number/T2OB54WTQDPY5PD2L3YINP6VK5/graph.json","events_json":"https://pith.science/api/pith-number/T2OB54WTQDPY5PD2L3YINP6VK5/events.json","paper":"https://pith.science/paper/T2OB54WT"},"agent_actions":{"view_html":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5","download_json":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5.json","view_paper":"https://pith.science/paper/T2OB54WT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.06513&json=true","fetch_graph":"https://pith.science/api/pith-number/T2OB54WTQDPY5PD2L3YINP6VK5/graph.json","fetch_events":"https://pith.science/api/pith-number/T2OB54WTQDPY5PD2L3YINP6VK5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5/action/storage_attestation","attest_author":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5/action/author_attestation","sign_citation":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5/action/citation_signature","submit_replication":"https://pith.science/pith/T2OB54WTQDPY5PD2L3YINP6VK5/action/replication_record"}},"created_at":"2026-07-05T04:50:12.344866+00:00","updated_at":"2026-07-05T04:50:12.344866+00:00"}