{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:T7DL5KGW5CAVCOEKTWGIIJJBRD","short_pith_number":"pith:T7DL5KGW","canonical_record":{"source":{"id":"1712.02036","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-06T04:36:40Z","cross_cats_sorted":[],"title_canon_sha256":"214b2ff3e725db9172a60d7eb9755885a33ed1ee6673d15709872b5e8c1059b0","abstract_canon_sha256":"fba1dd51d9b12489c4cda344cae5f2cbe4e4b23e3311f5ad7566d693e0861f55"},"schema_version":"1.0"},"canonical_sha256":"9fc6bea8d6e88151388a9d8c84252188e35cbfe63b34b07c9de121cd2edf6fad","source":{"kind":"arxiv","id":"1712.02036","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.02036","created_at":"2026-05-18T00:28:39Z"},{"alias_kind":"arxiv_version","alias_value":"1712.02036v1","created_at":"2026-05-18T00:28:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.02036","created_at":"2026-05-18T00:28:39Z"},{"alias_kind":"pith_short_12","alias_value":"T7DL5KGW5CAV","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"T7DL5KGW5CAVCOEK","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"T7DL5KGW","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:T7DL5KGW5CAVCOEKTWGIIJJBRD","target":"record","payload":{"canonical_record":{"source":{"id":"1712.02036","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-06T04:36:40Z","cross_cats_sorted":[],"title_canon_sha256":"214b2ff3e725db9172a60d7eb9755885a33ed1ee6673d15709872b5e8c1059b0","abstract_canon_sha256":"fba1dd51d9b12489c4cda344cae5f2cbe4e4b23e3311f5ad7566d693e0861f55"},"schema_version":"1.0"},"canonical_sha256":"9fc6bea8d6e88151388a9d8c84252188e35cbfe63b34b07c9de121cd2edf6fad","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:39.394120Z","signature_b64":"E5CX3LM6Ocjvi2ldiD5pH9xnchFGQcUovbR17VH6hr7we0DQzJ+Ia1vP5Q4Ny0/+0ffkeUXcerhMIpYuYOi5Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fc6bea8d6e88151388a9d8c84252188e35cbfe63b34b07c9de121cd2edf6fad","last_reissued_at":"2026-05-18T00:28:39.393481Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:39.393481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.02036","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":"2026-05-18T00:28:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R9wODrbmZvDEf1hsepwMwl6v6JXJWak0iJ8NWyEdSbr6SPVlmhVT41hrjUo+ohWGEpPBqLirC8oTfn5OiJElDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T22:24:43.150818Z"},"content_sha256":"240016901d8ceca3afb47e86b7a16dc5448057530a060cb3e641a9679e48e19b","schema_version":"1.0","event_id":"sha256:240016901d8ceca3afb47e86b7a16dc5448057530a060cb3e641a9679e48e19b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:T7DL5KGW5CAVCOEKTWGIIJJBRD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Semantic Concepts and Order for Image and Sentence Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liang Wang, Qi Wu, Yan Huang","submitted_at":"2017-12-06T04:36:40Z","abstract_excerpt":"Image and sentence matching has made great progress recently, but it remains challenging due to the large visual-semantic discrepancy. This mainly arises from that the representation of pixel-level image usually lacks of high-level semantic information as in its matched sentence. In this work, we propose a semantic-enhanced image and sentence matching model, which can improve the image representation by learning semantic concepts and then organizing them in a correct semantic order. Given an image, we first use a multi-regional multi-label CNN to predict its semantic concepts, including object"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.02036","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":"2026-05-18T00:28:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FtySWoi/pvY3j6Aan+cP5WvGy9e5+DrTqwTl/7vfkravICyo724YoBZPud14pH7/yDcQNaQZcVuCFJ970v4yBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T22:24:43.152240Z"},"content_sha256":"8ba9c039e0ac2caa8628419578d7d52d3dff866733260942560656eb7805f3d8","schema_version":"1.0","event_id":"sha256:8ba9c039e0ac2caa8628419578d7d52d3dff866733260942560656eb7805f3d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T7DL5KGW5CAVCOEKTWGIIJJBRD/bundle.json","state_url":"https://pith.science/pith/T7DL5KGW5CAVCOEKTWGIIJJBRD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T7DL5KGW5CAVCOEKTWGIIJJBRD/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":"2026-08-19T22:24:43Z","links":{"resolver":"https://pith.science/pith/T7DL5KGW5CAVCOEKTWGIIJJBRD","bundle":"https://pith.science/pith/T7DL5KGW5CAVCOEKTWGIIJJBRD/bundle.json","state":"https://pith.science/pith/T7DL5KGW5CAVCOEKTWGIIJJBRD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T7DL5KGW5CAVCOEKTWGIIJJBRD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:T7DL5KGW5CAVCOEKTWGIIJJBRD","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":"fba1dd51d9b12489c4cda344cae5f2cbe4e4b23e3311f5ad7566d693e0861f55","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-06T04:36:40Z","title_canon_sha256":"214b2ff3e725db9172a60d7eb9755885a33ed1ee6673d15709872b5e8c1059b0"},"schema_version":"1.0","source":{"id":"1712.02036","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.02036","created_at":"2026-05-18T00:28:39Z"},{"alias_kind":"arxiv_version","alias_value":"1712.02036v1","created_at":"2026-05-18T00:28:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.02036","created_at":"2026-05-18T00:28:39Z"},{"alias_kind":"pith_short_12","alias_value":"T7DL5KGW5CAV","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"T7DL5KGW5CAVCOEK","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"T7DL5KGW","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:8ba9c039e0ac2caa8628419578d7d52d3dff866733260942560656eb7805f3d8","target":"graph","created_at":"2026-05-18T00:28:39Z","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":"Image and sentence matching has made great progress recently, but it remains challenging due to the large visual-semantic discrepancy. This mainly arises from that the representation of pixel-level image usually lacks of high-level semantic information as in its matched sentence. In this work, we propose a semantic-enhanced image and sentence matching model, which can improve the image representation by learning semantic concepts and then organizing them in a correct semantic order. Given an image, we first use a multi-regional multi-label CNN to predict its semantic concepts, including object","authors_text":"Liang Wang, Qi Wu, Yan Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-06T04:36:40Z","title":"Learning Semantic Concepts and Order for Image and Sentence Matching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.02036","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":"sha256:240016901d8ceca3afb47e86b7a16dc5448057530a060cb3e641a9679e48e19b","target":"record","created_at":"2026-05-18T00:28:39Z","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":"fba1dd51d9b12489c4cda344cae5f2cbe4e4b23e3311f5ad7566d693e0861f55","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-06T04:36:40Z","title_canon_sha256":"214b2ff3e725db9172a60d7eb9755885a33ed1ee6673d15709872b5e8c1059b0"},"schema_version":"1.0","source":{"id":"1712.02036","kind":"arxiv","version":1}},"canonical_sha256":"9fc6bea8d6e88151388a9d8c84252188e35cbfe63b34b07c9de121cd2edf6fad","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9fc6bea8d6e88151388a9d8c84252188e35cbfe63b34b07c9de121cd2edf6fad","first_computed_at":"2026-05-18T00:28:39.393481Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:28:39.393481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E5CX3LM6Ocjvi2ldiD5pH9xnchFGQcUovbR17VH6hr7we0DQzJ+Ia1vP5Q4Ny0/+0ffkeUXcerhMIpYuYOi5Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:28:39.394120Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.02036","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:240016901d8ceca3afb47e86b7a16dc5448057530a060cb3e641a9679e48e19b","sha256:8ba9c039e0ac2caa8628419578d7d52d3dff866733260942560656eb7805f3d8"],"state_sha256":"20eb7f9f65bec93849c6e86ba57b6484991ba44cd6d2d4f3068cfc41125ecfba"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8eKoHAVH5VF7iiGexPxvX09InhqxRGvSBL1OQRJLHGJT+y6fV423oBca+POaB/EzaJvYAQLrovLac23reOmwBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T22:24:43.157490Z","bundle_sha256":"5e3cc239a9deab53ecca77e9135049f835e671b16dd1076a5d2391d0033fd190"}}