{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:WCQRWAWHO7OKLBL6YXC4YX2RRE","short_pith_number":"pith:WCQRWAWH","schema_version":"1.0","canonical_sha256":"b0a11b02c777dca5857ec5c5cc5f518936322fba1ad5484d9755c1cf91426ed7","source":{"kind":"arxiv","id":"1706.00153","version":2},"attestation_state":"computed","paper":{"title":"Cross-modal Common Representation Learning by Hybrid Transfer Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.MM","authors_text":"Mingkuan Yuan, Xin Huang, Yuxin Peng","submitted_at":"2017-06-01T02:53:57Z","abstract_excerpt":"DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-mo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1706.00153","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2017-06-01T02:53:57Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"756902d13d5cfe623ab9a62ed26a42aede2a46ad3cb28e36d1582584ce59d966","abstract_canon_sha256":"a9e20a04af0745be838d40b06e3f76eaf4d7a8eab833c09f886e5622b7a0a546"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:47.574590Z","signature_b64":"YGXmxb2vrzOQt+gcNmT8fwjAXvKpYh756BEzBMpiMPdIOXdTFUrvJjh4BEwFVeU4YN/N+o80ffcWIX4Ik7qDAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0a11b02c777dca5857ec5c5cc5f518936322fba1ad5484d9755c1cf91426ed7","last_reissued_at":"2026-05-18T00:41:47.574031Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:47.574031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-modal Common Representation Learning by Hybrid Transfer Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.MM","authors_text":"Mingkuan Yuan, Xin Huang, Yuxin Peng","submitted_at":"2017-06-01T02:53:57Z","abstract_excerpt":"DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00153","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":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1706.00153","created_at":"2026-05-18T00:41:47.574115+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.00153v2","created_at":"2026-05-18T00:41:47.574115+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00153","created_at":"2026-05-18T00:41:47.574115+00:00"},{"alias_kind":"pith_short_12","alias_value":"WCQRWAWHO7OK","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_16","alias_value":"WCQRWAWHO7OKLBL6","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_8","alias_value":"WCQRWAWH","created_at":"2026-05-18T12:31:53.515858+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE","json":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE.json","graph_json":"https://pith.science/api/pith-number/WCQRWAWHO7OKLBL6YXC4YX2RRE/graph.json","events_json":"https://pith.science/api/pith-number/WCQRWAWHO7OKLBL6YXC4YX2RRE/events.json","paper":"https://pith.science/paper/WCQRWAWH"},"agent_actions":{"view_html":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE","download_json":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE.json","view_paper":"https://pith.science/paper/WCQRWAWH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.00153&json=true","fetch_graph":"https://pith.science/api/pith-number/WCQRWAWHO7OKLBL6YXC4YX2RRE/graph.json","fetch_events":"https://pith.science/api/pith-number/WCQRWAWHO7OKLBL6YXC4YX2RRE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE/action/storage_attestation","attest_author":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE/action/author_attestation","sign_citation":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE/action/citation_signature","submit_replication":"https://pith.science/pith/WCQRWAWHO7OKLBL6YXC4YX2RRE/action/replication_record"}},"created_at":"2026-05-18T00:41:47.574115+00:00","updated_at":"2026-05-18T00:41:47.574115+00:00"}