{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SML5GPGBDKGHAVO54VUZQLGLS7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ee2cb99f4831f5836e17e3615aac3c8d9a626927a1d2eb8992bfa2a6e81dee0b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-08T21:18:23Z","title_canon_sha256":"8ac541bc26203c8ee0f09ede835e93872ba3cf6c0e42c7424ad936c4fa071a6f"},"schema_version":"1.0","source":{"id":"2308.04589","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.04589","created_at":"2026-07-05T06:43:05Z"},{"alias_kind":"arxiv_version","alias_value":"2308.04589v2","created_at":"2026-07-05T06:43:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.04589","created_at":"2026-07-05T06:43:05Z"},{"alias_kind":"pith_short_12","alias_value":"SML5GPGBDKGH","created_at":"2026-07-05T06:43:05Z"},{"alias_kind":"pith_short_16","alias_value":"SML5GPGBDKGHAVO5","created_at":"2026-07-05T06:43:05Z"},{"alias_kind":"pith_short_8","alias_value":"SML5GPGB","created_at":"2026-07-05T06:43:05Z"}],"graph_snapshots":[{"event_id":"sha256:b0237424fd89cfd53d9696430040593f62726a7436504b3b786e3315deef411e","target":"graph","created_at":"2026-07-05T06:43:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2308.04589/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The emerging field of action prediction plays a vital role in various computer vision applications such as autonomous driving, activity analysis and human-computer interaction. Despite significant advancements, accurately predicting future actions remains a challenging problem due to high dimensionality, complex dynamics and uncertainties inherent in video data. Traditional supervised approaches require large amounts of labelled data, which is expensive and time-consuming to obtain. This paper introduces a novel self-supervised video strategy for enhancing action prediction inspired by DINO (s","authors_text":"Andrew Bradley, Biplab Banerjee, Fabio Cuzzolin, Izzeddin Teeti, Rongali Sai Bhargav, Vivek Singh","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-08T21:18:23Z","title":"Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.04589","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a6785b21c78155731696a86730e09891b6954a0ddaa8af206d14da4e3f390600","target":"record","created_at":"2026-07-05T06:43:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ee2cb99f4831f5836e17e3615aac3c8d9a626927a1d2eb8992bfa2a6e81dee0b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-08T21:18:23Z","title_canon_sha256":"8ac541bc26203c8ee0f09ede835e93872ba3cf6c0e42c7424ad936c4fa071a6f"},"schema_version":"1.0","source":{"id":"2308.04589","kind":"arxiv","version":2}},"canonical_sha256":"9317d33cc11a8c7055dde569982ccb97ed906c39ab0d94ee935d6d1a9eca8634","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9317d33cc11a8c7055dde569982ccb97ed906c39ab0d94ee935d6d1a9eca8634","first_computed_at":"2026-07-05T06:43:05.281262Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:43:05.281262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"au3wJHONdJzJa8XHO1AWU3AiuFddyiIv56fZIjqghiEMHhwUHTSq3edRHCZVAukxKp/B0/axV9w+Ir/kGCjoBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:43:05.281697Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.04589","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a6785b21c78155731696a86730e09891b6954a0ddaa8af206d14da4e3f390600","sha256:b0237424fd89cfd53d9696430040593f62726a7436504b3b786e3315deef411e"],"state_sha256":"f39f681644859232553ab7f3654c3c3b6b3074817c542bb3f09588ae89b42261"}