{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:FSC2B5U3FPHSKOXJMECSQEW6LR","short_pith_number":"pith:FSC2B5U3","canonical_record":{"source":{"id":"2002.06353","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-15T10:03:25Z","cross_cats_sorted":["cs.CL","cs.LG","eess.AS","eess.IV"],"title_canon_sha256":"eb272caaf0fb4145d60929f913544031cfff08dd779727b8e66f2514df1105a3","abstract_canon_sha256":"f8406ad3c0b6b3d204035dbd6cad99525ff44a2d762278d5deccf1c8a9402562"},"schema_version":"1.0"},"canonical_sha256":"2c85a0f69b2bcf253ae961052812de5c65378f6d1dee1e17384e237f29139041","source":{"kind":"arxiv","id":"2002.06353","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.06353","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"arxiv_version","alias_value":"2002.06353v3","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.06353","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"pith_short_12","alias_value":"FSC2B5U3FPHS","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"pith_short_16","alias_value":"FSC2B5U3FPHSKOXJ","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"pith_short_8","alias_value":"FSC2B5U3","created_at":"2026-07-05T01:35:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:FSC2B5U3FPHSKOXJMECSQEW6LR","target":"record","payload":{"canonical_record":{"source":{"id":"2002.06353","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-15T10:03:25Z","cross_cats_sorted":["cs.CL","cs.LG","eess.AS","eess.IV"],"title_canon_sha256":"eb272caaf0fb4145d60929f913544031cfff08dd779727b8e66f2514df1105a3","abstract_canon_sha256":"f8406ad3c0b6b3d204035dbd6cad99525ff44a2d762278d5deccf1c8a9402562"},"schema_version":"1.0"},"canonical_sha256":"2c85a0f69b2bcf253ae961052812de5c65378f6d1dee1e17384e237f29139041","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:35:28.662709Z","signature_b64":"KgVv3H8lhytUsxtrSGUZpVVC/6zfwapxI+qIsSIRwSxybNWzHSlYQ/rlpt5NRKt5cn4U9RgQbu8iZ81OlAJpDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c85a0f69b2bcf253ae961052812de5c65378f6d1dee1e17384e237f29139041","last_reissued_at":"2026-07-05T01:35:28.662190Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:35:28.662190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.06353","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:35:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ds1oOCp/+qjyJSe1IXaCxgi2o/TFCbHEGB6WqOXmY3xYtlyFWSev5LwJCEsVgzIEEj4TAUBomBo1tJ/rTSMyAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:46:13.790661Z"},"content_sha256":"ea3c9a1a6340bb75eee7d0ba009c43c028992b40bea9b981bac482bf6d7fd04e","schema_version":"1.0","event_id":"sha256:ea3c9a1a6340bb75eee7d0ba009c43c028992b40bea9b981bac482bf6d7fd04e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:FSC2B5U3FPHSKOXJMECSQEW6LR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","eess.AS","eess.IV"],"primary_cat":"cs.CV","authors_text":"Botian Shi, Haoyang Huang, Huaishao Luo, Jason Li, Lei Ji, Ming Zhou, Nan Duan, Taroon Bharti, Tianrui Li","submitted_at":"2020-02-15T10:03:25Z","abstract_excerpt":"With the recent success of the pre-training technique for NLP and image-linguistic tasks, some video-linguistic pre-training works are gradually developed to improve video-text related downstream tasks. However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks. This paper proposes UniVL: a Unified Video and Language pre-training model for both multimodal understanding and generation. It comprises four components, including two single-modal encoders, a cross encoder, and a decoder with the Transformer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.06353","kind":"arxiv","version":3},"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/2002.06353/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:35:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9fw6n2+T10e+HNdBmys4Ty9QyhdliN7hGDEPqY3Rl+sRZb+/6DPALIzFOuoxMNVxnEsBkEdq4umfmnGqYGoPDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:46:13.791263Z"},"content_sha256":"4e4056542c096f7861832116b44740e6a9d6efa56b8ee3b04c483b02db12219e","schema_version":"1.0","event_id":"sha256:4e4056542c096f7861832116b44740e6a9d6efa56b8ee3b04c483b02db12219e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FSC2B5U3FPHSKOXJMECSQEW6LR/bundle.json","state_url":"https://pith.science/pith/FSC2B5U3FPHSKOXJMECSQEW6LR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FSC2B5U3FPHSKOXJMECSQEW6LR/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":"2026-07-09T06:46:13Z","links":{"resolver":"https://pith.science/pith/FSC2B5U3FPHSKOXJMECSQEW6LR","bundle":"https://pith.science/pith/FSC2B5U3FPHSKOXJMECSQEW6LR/bundle.json","state":"https://pith.science/pith/FSC2B5U3FPHSKOXJMECSQEW6LR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FSC2B5U3FPHSKOXJMECSQEW6LR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:FSC2B5U3FPHSKOXJMECSQEW6LR","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":"f8406ad3c0b6b3d204035dbd6cad99525ff44a2d762278d5deccf1c8a9402562","cross_cats_sorted":["cs.CL","cs.LG","eess.AS","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-15T10:03:25Z","title_canon_sha256":"eb272caaf0fb4145d60929f913544031cfff08dd779727b8e66f2514df1105a3"},"schema_version":"1.0","source":{"id":"2002.06353","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.06353","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"arxiv_version","alias_value":"2002.06353v3","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.06353","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"pith_short_12","alias_value":"FSC2B5U3FPHS","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"pith_short_16","alias_value":"FSC2B5U3FPHSKOXJ","created_at":"2026-07-05T01:35:28Z"},{"alias_kind":"pith_short_8","alias_value":"FSC2B5U3","created_at":"2026-07-05T01:35:28Z"}],"graph_snapshots":[{"event_id":"sha256:4e4056542c096f7861832116b44740e6a9d6efa56b8ee3b04c483b02db12219e","target":"graph","created_at":"2026-07-05T01:35:28Z","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/2002.06353/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the recent success of the pre-training technique for NLP and image-linguistic tasks, some video-linguistic pre-training works are gradually developed to improve video-text related downstream tasks. However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks. This paper proposes UniVL: a Unified Video and Language pre-training model for both multimodal understanding and generation. It comprises four components, including two single-modal encoders, a cross encoder, and a decoder with the Transformer","authors_text":"Botian Shi, Haoyang Huang, Huaishao Luo, Jason Li, Lei Ji, Ming Zhou, Nan Duan, Taroon Bharti, Tianrui Li","cross_cats":["cs.CL","cs.LG","eess.AS","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-15T10:03:25Z","title":"UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.06353","kind":"arxiv","version":3},"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:ea3c9a1a6340bb75eee7d0ba009c43c028992b40bea9b981bac482bf6d7fd04e","target":"record","created_at":"2026-07-05T01:35:28Z","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":"f8406ad3c0b6b3d204035dbd6cad99525ff44a2d762278d5deccf1c8a9402562","cross_cats_sorted":["cs.CL","cs.LG","eess.AS","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-15T10:03:25Z","title_canon_sha256":"eb272caaf0fb4145d60929f913544031cfff08dd779727b8e66f2514df1105a3"},"schema_version":"1.0","source":{"id":"2002.06353","kind":"arxiv","version":3}},"canonical_sha256":"2c85a0f69b2bcf253ae961052812de5c65378f6d1dee1e17384e237f29139041","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c85a0f69b2bcf253ae961052812de5c65378f6d1dee1e17384e237f29139041","first_computed_at":"2026-07-05T01:35:28.662190Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:35:28.662190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KgVv3H8lhytUsxtrSGUZpVVC/6zfwapxI+qIsSIRwSxybNWzHSlYQ/rlpt5NRKt5cn4U9RgQbu8iZ81OlAJpDA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:35:28.662709Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.06353","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea3c9a1a6340bb75eee7d0ba009c43c028992b40bea9b981bac482bf6d7fd04e","sha256:4e4056542c096f7861832116b44740e6a9d6efa56b8ee3b04c483b02db12219e"],"state_sha256":"97b825915de614931c053107937a1a5fd35a2349c0bbdf294ed92d05ad1cbfd5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pZNbDndAN512TZ6nYY5HvIe8nmkihKGQUpb7nRzAWX9gF37IG9+wlZSHbxOSgGm56t6cFmlenIkHem8hloBgAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:46:13.794641Z","bundle_sha256":"239c90a4b8a5dda57f7f7c91ecbf1bf9eb200bbf25e37eff18227cb724ce4821"}}