{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:IYYJFQ6SCY44BMRRF2X7REIBTZ","short_pith_number":"pith:IYYJFQ6S","canonical_record":{"source":{"id":"2101.12059","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-01-28T15:22:36Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"9f18bb3803dacd0aa36d27255c261560c6e07da31c1c07dc093d5a8f3dc157bc","abstract_canon_sha256":"9ef07deefcad07d2d6ae8cd5eb8850f46777ecf2bbeeab89f243551007d8b0ea"},"schema_version":"1.0"},"canonical_sha256":"463092c3d21639c0b2312eaff891019e72d412930cb16667673c360ab90b634f","source":{"kind":"arxiv","id":"2101.12059","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.12059","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"arxiv_version","alias_value":"2101.12059v2","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.12059","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"pith_short_12","alias_value":"IYYJFQ6SCY44","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"pith_short_16","alias_value":"IYYJFQ6SCY44BMRR","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"pith_short_8","alias_value":"IYYJFQ6S","created_at":"2026-07-05T02:10:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:IYYJFQ6SCY44BMRRF2X7REIBTZ","target":"record","payload":{"canonical_record":{"source":{"id":"2101.12059","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-01-28T15:22:36Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"9f18bb3803dacd0aa36d27255c261560c6e07da31c1c07dc093d5a8f3dc157bc","abstract_canon_sha256":"9ef07deefcad07d2d6ae8cd5eb8850f46777ecf2bbeeab89f243551007d8b0ea"},"schema_version":"1.0"},"canonical_sha256":"463092c3d21639c0b2312eaff891019e72d412930cb16667673c360ab90b634f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:10:49.419058Z","signature_b64":"Jn7n9imIQMlUrpsJaxzYPBs9BRpvyxXOGY0qHOd4Nq5WZD05sw0v8ezPnVu5HxSjcPfM7jQ6sBupknsIFvkXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"463092c3d21639c0b2312eaff891019e72d412930cb16667673c360ab90b634f","last_reissued_at":"2026-07-05T02:10:49.418648Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:10:49.418648Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.12059","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:10:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uuVUn73X//+DjNUSXN3cK6JsvuXpcbfrerV3G+oG9NWLVoEZ2zbsRJCh5BjvF+C4oC8B0FiwulQ6kDfxQhobBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:48:19.589288Z"},"content_sha256":"07ec9ed1b80e9c4f80a22dd56ef23715c6edbd915f1c2d002ed3c3bee24f06e6","schema_version":"1.0","event_id":"sha256:07ec9ed1b80e9c4f80a22dd56ef23715c6edbd915f1c2d002ed3c3bee24f06e6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:IYYJFQ6SCY44BMRRF2X7REIBTZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Devi Parikh, Gedas Bertasius, Jue Wang, Lorenzo Torresani, Shih-Fu Chang, Xudong Lin","submitted_at":"2021-01-28T15:22:36Z","abstract_excerpt":"We present \\textsc{Vx2Text}, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. In order to leverage transformer networks, which have been shown to be effective at modeling language, each modality is first converted into a set of language embeddings by a learnable tokenizer. This allows our approach to perform multimodal fusion in the language space, thus eliminating the need for ad-hoc cross-modal fusion modules. To address the non-differentiability of tokenization on continuous inputs (e.g., video or audio), we utilize a relaxation scheme "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.12059","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/2101.12059/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":"2026-07-05T02:10:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xWFE1bBtru/epFKPwvqZueq94czLrAftdXiez8mGo6PNmo2WM3zoWDSL1Vp8xLh/dCf39AqlO2kCBgmuE9YmDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:48:19.589842Z"},"content_sha256":"953edfdc2e7eed79458437f6323856ffd7b41b44e086f8ff498bdc7b708e3fd3","schema_version":"1.0","event_id":"sha256:953edfdc2e7eed79458437f6323856ffd7b41b44e086f8ff498bdc7b708e3fd3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IYYJFQ6SCY44BMRRF2X7REIBTZ/bundle.json","state_url":"https://pith.science/pith/IYYJFQ6SCY44BMRRF2X7REIBTZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IYYJFQ6SCY44BMRRF2X7REIBTZ/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":"2026-08-05T14:48:19Z","links":{"resolver":"https://pith.science/pith/IYYJFQ6SCY44BMRRF2X7REIBTZ","bundle":"https://pith.science/pith/IYYJFQ6SCY44BMRRF2X7REIBTZ/bundle.json","state":"https://pith.science/pith/IYYJFQ6SCY44BMRRF2X7REIBTZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IYYJFQ6SCY44BMRRF2X7REIBTZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:IYYJFQ6SCY44BMRRF2X7REIBTZ","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":"9ef07deefcad07d2d6ae8cd5eb8850f46777ecf2bbeeab89f243551007d8b0ea","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-01-28T15:22:36Z","title_canon_sha256":"9f18bb3803dacd0aa36d27255c261560c6e07da31c1c07dc093d5a8f3dc157bc"},"schema_version":"1.0","source":{"id":"2101.12059","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.12059","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"arxiv_version","alias_value":"2101.12059v2","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.12059","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"pith_short_12","alias_value":"IYYJFQ6SCY44","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"pith_short_16","alias_value":"IYYJFQ6SCY44BMRR","created_at":"2026-07-05T02:10:49Z"},{"alias_kind":"pith_short_8","alias_value":"IYYJFQ6S","created_at":"2026-07-05T02:10:49Z"}],"graph_snapshots":[{"event_id":"sha256:953edfdc2e7eed79458437f6323856ffd7b41b44e086f8ff498bdc7b708e3fd3","target":"graph","created_at":"2026-07-05T02:10:49Z","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/2101.12059/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present \\textsc{Vx2Text}, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. In order to leverage transformer networks, which have been shown to be effective at modeling language, each modality is first converted into a set of language embeddings by a learnable tokenizer. This allows our approach to perform multimodal fusion in the language space, thus eliminating the need for ad-hoc cross-modal fusion modules. To address the non-differentiability of tokenization on continuous inputs (e.g., video or audio), we utilize a relaxation scheme ","authors_text":"Devi Parikh, Gedas Bertasius, Jue Wang, Lorenzo Torresani, Shih-Fu Chang, Xudong Lin","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-01-28T15:22:36Z","title":"VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.12059","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":"sha256:07ec9ed1b80e9c4f80a22dd56ef23715c6edbd915f1c2d002ed3c3bee24f06e6","target":"record","created_at":"2026-07-05T02:10:49Z","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":"9ef07deefcad07d2d6ae8cd5eb8850f46777ecf2bbeeab89f243551007d8b0ea","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-01-28T15:22:36Z","title_canon_sha256":"9f18bb3803dacd0aa36d27255c261560c6e07da31c1c07dc093d5a8f3dc157bc"},"schema_version":"1.0","source":{"id":"2101.12059","kind":"arxiv","version":2}},"canonical_sha256":"463092c3d21639c0b2312eaff891019e72d412930cb16667673c360ab90b634f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"463092c3d21639c0b2312eaff891019e72d412930cb16667673c360ab90b634f","first_computed_at":"2026-07-05T02:10:49.418648Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:10:49.418648Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Jn7n9imIQMlUrpsJaxzYPBs9BRpvyxXOGY0qHOd4Nq5WZD05sw0v8ezPnVu5HxSjcPfM7jQ6sBupknsIFvkXBg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:10:49.419058Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.12059","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07ec9ed1b80e9c4f80a22dd56ef23715c6edbd915f1c2d002ed3c3bee24f06e6","sha256:953edfdc2e7eed79458437f6323856ffd7b41b44e086f8ff498bdc7b708e3fd3"],"state_sha256":"c2b1d00a6a97229ca439fa01bd98cb01685c21080b3fa4dc5f6376d306bf380b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cBOUYJVFHnWY+ucjKuTxVkvJxL6LoDF8y+1PAYHo2iOOudzWPMVWS0ukzt43y3stsYU/0W4LIQHD1U1ejXPTBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:48:19.593344Z","bundle_sha256":"aea976a78543f7adadb79546daa2add5ebaed59f9f072a2d921c63a8280a768a"}}