{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:YEPXO34ZIV7WEZLDTUOADBKLR6","short_pith_number":"pith:YEPXO34Z","canonical_record":{"source":{"id":"1905.10000","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-24T02:02:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f10e44ffc49016109006c92fa9762d83159be89a848021e922209d2350b3d50e","abstract_canon_sha256":"c5784280bbb857298a25ca0cade87f464029c776f5122b4f74d8774c7395a865"},"schema_version":"1.0"},"canonical_sha256":"c11f776f99457f6265639d1c01854b8f9678af04d301bbff4cb6ddee07fdbfae","source":{"kind":"arxiv","id":"1905.10000","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10000","created_at":"2026-05-17T23:45:13Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10000v1","created_at":"2026-05-17T23:45:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10000","created_at":"2026-05-17T23:45:13Z"},{"alias_kind":"pith_short_12","alias_value":"YEPXO34ZIV7W","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"YEPXO34ZIV7WEZLD","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"YEPXO34Z","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:YEPXO34ZIV7WEZLDTUOADBKLR6","target":"record","payload":{"canonical_record":{"source":{"id":"1905.10000","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-24T02:02:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f10e44ffc49016109006c92fa9762d83159be89a848021e922209d2350b3d50e","abstract_canon_sha256":"c5784280bbb857298a25ca0cade87f464029c776f5122b4f74d8774c7395a865"},"schema_version":"1.0"},"canonical_sha256":"c11f776f99457f6265639d1c01854b8f9678af04d301bbff4cb6ddee07fdbfae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:13.806838Z","signature_b64":"/ZVlsRpPz/2yOV/fFCxbSjImSGY5JakJdzB+VV6dIJG0z5voAUvedzimk0r1gX9q0Vqz6NSuNpgMBvZZkK3KAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c11f776f99457f6265639d1c01854b8f9678af04d301bbff4cb6ddee07fdbfae","last_reissued_at":"2026-05-17T23:45:13.806238Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:13.806238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.10000","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:45:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lCEIAaDZajENRQ8BlcAfxJgkkhZCjNuSkeQSQv2QNCIoMdfo1rd4Ldd5b/pO7w/JTdlsFR8UL+ONThEnzMiZBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T18:00:48.070209Z"},"content_sha256":"c1c7f70cd74164c97e4d303ed12cc6d356dbebbcd69e663350db0b6643a9ef7a","schema_version":"1.0","event_id":"sha256:c1c7f70cd74164c97e4d303ed12cc6d356dbebbcd69e663350db0b6643a9ef7a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:YEPXO34ZIV7WEZLDTUOADBKLR6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Tara Javidi, Yongxi Lu, Ziyao Tang","submitted_at":"2019-05-24T02:02:35Z","abstract_excerpt":"Partially annotated clips contain rich temporal contexts that can complement the sparse key frame annotations in providing supervision for model training. We present a novel paradigm called Temporally-Adaptive Features (TAF) learning that can utilize such data to learn better single frame models. By imposing distinct temporal change rate constraints on different factors in the model, TAF enables learning from unlabeled frames using context to enhance model accuracy. TAF generalizes \"slow feature\" learning and we present much stronger empirical evidence than prior works, showing convincing gain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10000","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:45:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rh9sb8ykVKXjNDVEggf+Zbh9CJkku3s19N4HUyoHSlxRcFeQQSVqFnd73UnLg1p+7Wi9p315L6w+9mKqiKEaBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T18:00:48.070940Z"},"content_sha256":"7070ff5295afd3d57c6bd4cad298ff1ef6b5974be36bbfc2ba3309effd0895e1","schema_version":"1.0","event_id":"sha256:7070ff5295afd3d57c6bd4cad298ff1ef6b5974be36bbfc2ba3309effd0895e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YEPXO34ZIV7WEZLDTUOADBKLR6/bundle.json","state_url":"https://pith.science/pith/YEPXO34ZIV7WEZLDTUOADBKLR6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YEPXO34ZIV7WEZLDTUOADBKLR6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T18:00:48Z","links":{"resolver":"https://pith.science/pith/YEPXO34ZIV7WEZLDTUOADBKLR6","bundle":"https://pith.science/pith/YEPXO34ZIV7WEZLDTUOADBKLR6/bundle.json","state":"https://pith.science/pith/YEPXO34ZIV7WEZLDTUOADBKLR6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YEPXO34ZIV7WEZLDTUOADBKLR6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:YEPXO34ZIV7WEZLDTUOADBKLR6","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":"c5784280bbb857298a25ca0cade87f464029c776f5122b4f74d8774c7395a865","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-24T02:02:35Z","title_canon_sha256":"f10e44ffc49016109006c92fa9762d83159be89a848021e922209d2350b3d50e"},"schema_version":"1.0","source":{"id":"1905.10000","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.10000","created_at":"2026-05-17T23:45:13Z"},{"alias_kind":"arxiv_version","alias_value":"1905.10000v1","created_at":"2026-05-17T23:45:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10000","created_at":"2026-05-17T23:45:13Z"},{"alias_kind":"pith_short_12","alias_value":"YEPXO34ZIV7W","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"YEPXO34ZIV7WEZLD","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"YEPXO34Z","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:7070ff5295afd3d57c6bd4cad298ff1ef6b5974be36bbfc2ba3309effd0895e1","target":"graph","created_at":"2026-05-17T23:45:13Z","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"},"paper":{"abstract_excerpt":"Partially annotated clips contain rich temporal contexts that can complement the sparse key frame annotations in providing supervision for model training. We present a novel paradigm called Temporally-Adaptive Features (TAF) learning that can utilize such data to learn better single frame models. By imposing distinct temporal change rate constraints on different factors in the model, TAF enables learning from unlabeled frames using context to enhance model accuracy. TAF generalizes \"slow feature\" learning and we present much stronger empirical evidence than prior works, showing convincing gain","authors_text":"Tara Javidi, Yongxi Lu, Ziyao Tang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-24T02:02:35Z","title":"Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10000","kind":"arxiv","version":1},"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:c1c7f70cd74164c97e4d303ed12cc6d356dbebbcd69e663350db0b6643a9ef7a","target":"record","created_at":"2026-05-17T23:45:13Z","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":"c5784280bbb857298a25ca0cade87f464029c776f5122b4f74d8774c7395a865","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-24T02:02:35Z","title_canon_sha256":"f10e44ffc49016109006c92fa9762d83159be89a848021e922209d2350b3d50e"},"schema_version":"1.0","source":{"id":"1905.10000","kind":"arxiv","version":1}},"canonical_sha256":"c11f776f99457f6265639d1c01854b8f9678af04d301bbff4cb6ddee07fdbfae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c11f776f99457f6265639d1c01854b8f9678af04d301bbff4cb6ddee07fdbfae","first_computed_at":"2026-05-17T23:45:13.806238Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:45:13.806238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/ZVlsRpPz/2yOV/fFCxbSjImSGY5JakJdzB+VV6dIJG0z5voAUvedzimk0r1gX9q0Vqz6NSuNpgMBvZZkK3KAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:45:13.806838Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.10000","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1c7f70cd74164c97e4d303ed12cc6d356dbebbcd69e663350db0b6643a9ef7a","sha256:7070ff5295afd3d57c6bd4cad298ff1ef6b5974be36bbfc2ba3309effd0895e1"],"state_sha256":"8dbaab47fdcd48b3dd284c96fcb44547f577c95003aa9ee119ced81e139ffcb9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3M9hp9WM62po40aX8nSGbK3nxSQK9XJmV7YjkAmVz2GTrhxZk/HtVsPZSBMM5w1gAZzNHNsvXKkuCP/ypkrZCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T18:00:48.075225Z","bundle_sha256":"f2ddf13a5703762b1bcdb2b0b0c95e565d5572f4568824317ab0df8d37c63980"}}