{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:3IKWCWD3PDTWMCNWSDS7M4C7HR","short_pith_number":"pith:3IKWCWD3","canonical_record":{"source":{"id":"2103.08784","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-03-16T00:35:28Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"1325a5414837da20e6940ff7fbb6dd894df95b111619c7e50d5b1da029ac4400","abstract_canon_sha256":"44392102f5e1e057cdb508757799bd36257656bb33971d890a4f52f8efb8333a"},"schema_version":"1.0"},"canonical_sha256":"da1561587b78e76609b690e5f6705f3c4858d71078b7ba0ccc3335c6e5b9a077","source":{"kind":"arxiv","id":"2103.08784","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.08784","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"arxiv_version","alias_value":"2103.08784v2","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.08784","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"pith_short_12","alias_value":"3IKWCWD3PDTW","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"pith_short_16","alias_value":"3IKWCWD3PDTWMCNW","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"pith_short_8","alias_value":"3IKWCWD3","created_at":"2026-07-05T02:31:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:3IKWCWD3PDTWMCNWSDS7M4C7HR","target":"record","payload":{"canonical_record":{"source":{"id":"2103.08784","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-03-16T00:35:28Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"1325a5414837da20e6940ff7fbb6dd894df95b111619c7e50d5b1da029ac4400","abstract_canon_sha256":"44392102f5e1e057cdb508757799bd36257656bb33971d890a4f52f8efb8333a"},"schema_version":"1.0"},"canonical_sha256":"da1561587b78e76609b690e5f6705f3c4858d71078b7ba0ccc3335c6e5b9a077","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:31:04.802193Z","signature_b64":"8y+cJtCdahwwFvrOQbL/MrHcA0hoWCMaRmFEWMeFVPFH2HVIVhXI9kqYAvW8US3UwNRKNwl7Vr2t3XW52vQuAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da1561587b78e76609b690e5f6705f3c4858d71078b7ba0ccc3335c6e5b9a077","last_reissued_at":"2026-07-05T02:31:04.801713Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:31:04.801713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.08784","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":"2026-07-05T02:31:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dFH8ljcbamG05ywwtzNFPH6Qb5MU77kNMtNA5WVQlge6vfQCAYNxQU0jl/Ep9wtEgYV2UKd/hXrgvguU39qeBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T20:42:52.239111Z"},"content_sha256":"bd9dec60812ce15fa5c29001548bf7a71bb123f0f6bc99d781fba21eca368ed1","schema_version":"1.0","event_id":"sha256:bd9dec60812ce15fa5c29001548bf7a71bb123f0f6bc99d781fba21eca368ed1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:3IKWCWD3PDTWMCNWSDS7M4C7HR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Jingjing Liu, Linjie Li, Shuohang Wang, Siqi Sun, Yen-Chun Chen, Yuwei Fang","submitted_at":"2021-03-16T00:35:28Z","abstract_excerpt":"Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computation cost mainly from cross-modal attention in Transformer architecture. When applied to real-life applications, such latency and computation demand severely deter the practical use of pre-trained models. In this paper, we study Image-text retrieval (ITR), the most mature scenario of V+L application, which has been widely studied even prior to the emergence of recent pre-trained mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.08784","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/2103.08784/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":"2026-07-05T02:31:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RStQp+pueR8B+gDh5Oqna9ev3KP7PsYS3lq3Y/9Ejot8oJic+wurMqCyb4QM9doIMVJ+363TOy3a6HXzeleLBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T20:42:52.239659Z"},"content_sha256":"351a2e357ad0d3d2498d56f145e0f7d93d172745aab3ac2d7da86837c2a12a3d","schema_version":"1.0","event_id":"sha256:351a2e357ad0d3d2498d56f145e0f7d93d172745aab3ac2d7da86837c2a12a3d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3IKWCWD3PDTWMCNWSDS7M4C7HR/bundle.json","state_url":"https://pith.science/pith/3IKWCWD3PDTWMCNWSDS7M4C7HR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3IKWCWD3PDTWMCNWSDS7M4C7HR/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":"2026-08-05T20:42:52Z","links":{"resolver":"https://pith.science/pith/3IKWCWD3PDTWMCNWSDS7M4C7HR","bundle":"https://pith.science/pith/3IKWCWD3PDTWMCNWSDS7M4C7HR/bundle.json","state":"https://pith.science/pith/3IKWCWD3PDTWMCNWSDS7M4C7HR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3IKWCWD3PDTWMCNWSDS7M4C7HR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3IKWCWD3PDTWMCNWSDS7M4C7HR","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":"44392102f5e1e057cdb508757799bd36257656bb33971d890a4f52f8efb8333a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-03-16T00:35:28Z","title_canon_sha256":"1325a5414837da20e6940ff7fbb6dd894df95b111619c7e50d5b1da029ac4400"},"schema_version":"1.0","source":{"id":"2103.08784","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.08784","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"arxiv_version","alias_value":"2103.08784v2","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.08784","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"pith_short_12","alias_value":"3IKWCWD3PDTW","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"pith_short_16","alias_value":"3IKWCWD3PDTWMCNW","created_at":"2026-07-05T02:31:04Z"},{"alias_kind":"pith_short_8","alias_value":"3IKWCWD3","created_at":"2026-07-05T02:31:04Z"}],"graph_snapshots":[{"event_id":"sha256:351a2e357ad0d3d2498d56f145e0f7d93d172745aab3ac2d7da86837c2a12a3d","target":"graph","created_at":"2026-07-05T02:31:04Z","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/2103.08784/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computation cost mainly from cross-modal attention in Transformer architecture. When applied to real-life applications, such latency and computation demand severely deter the practical use of pre-trained models. In this paper, we study Image-text retrieval (ITR), the most mature scenario of V+L application, which has been widely studied even prior to the emergence of recent pre-trained mode","authors_text":"Jingjing Liu, Linjie Li, Shuohang Wang, Siqi Sun, Yen-Chun Chen, Yuwei Fang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-03-16T00:35:28Z","title":"LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.08784","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:bd9dec60812ce15fa5c29001548bf7a71bb123f0f6bc99d781fba21eca368ed1","target":"record","created_at":"2026-07-05T02:31:04Z","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":"44392102f5e1e057cdb508757799bd36257656bb33971d890a4f52f8efb8333a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-03-16T00:35:28Z","title_canon_sha256":"1325a5414837da20e6940ff7fbb6dd894df95b111619c7e50d5b1da029ac4400"},"schema_version":"1.0","source":{"id":"2103.08784","kind":"arxiv","version":2}},"canonical_sha256":"da1561587b78e76609b690e5f6705f3c4858d71078b7ba0ccc3335c6e5b9a077","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"da1561587b78e76609b690e5f6705f3c4858d71078b7ba0ccc3335c6e5b9a077","first_computed_at":"2026-07-05T02:31:04.801713Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:31:04.801713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8y+cJtCdahwwFvrOQbL/MrHcA0hoWCMaRmFEWMeFVPFH2HVIVhXI9kqYAvW8US3UwNRKNwl7Vr2t3XW52vQuAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:31:04.802193Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.08784","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd9dec60812ce15fa5c29001548bf7a71bb123f0f6bc99d781fba21eca368ed1","sha256:351a2e357ad0d3d2498d56f145e0f7d93d172745aab3ac2d7da86837c2a12a3d"],"state_sha256":"1ac7e625ceb0c240a18d786c23abafc1ab0a45a594c5fe41a12764b44da46165"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nYfuJgR+kiqO2enCpQjTfrxmWu/bvgHHziYiaz+wZCj1c2uRqe4z6V7/wuxAm2RZHId0IPmMeAvpIRHljdXxDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T20:42:52.244867Z","bundle_sha256":"5bd167592dd82f1e670d22c265f3938cbc25f239becb63e04cbb70c7367ad398"}}