{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:35YU4KUCTYBJ6W7I6BHTRPAGMI","short_pith_number":"pith:35YU4KUC","schema_version":"1.0","canonical_sha256":"df714e2a829e029f5be8f04f38bc06620530022d00bace8205fc46cc4117877f","source":{"kind":"arxiv","id":"1903.11701","version":1},"attestation_state":"computed","paper":{"title":"Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"C. S. George Lee, Debasmit Das","submitted_at":"2019-03-27T21:07:00Z","abstract_excerpt":"Zero-shot learning (ZSL) for image classification focuses on recognizing novel categories that have no labeled data available for training. The learning is generally carried out with the help of mid-level semantic descriptors associated with each class. This semantic-descriptor space is generally shared by both seen and unseen categories. However, ZSL suffers from hubness, domain discrepancy and biased-ness towards seen classes. To tackle these problems, we propose a three-step approach to zero-shot learning. Firstly, a mapping is learned from the semantic-descriptor space to the image-feature"},"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":"1903.11701","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-27T21:07:00Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3f98ededa0bb6038280d7dde659198ef8cf2ad76d784cac057be2309ad096898","abstract_canon_sha256":"5e75241012114c626af0038f64cfe8392e12045619e280c7fc01cc2cd59a48eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:01.984426Z","signature_b64":"ewQ+YHT6ZSsrSopOsnZ+U70ce7Ri+BhN6RokO03NdBetH60pL36HHNdFJyn+/zSECb/idNYtTkrHaoE80/w1BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df714e2a829e029f5be8f04f38bc06620530022d00bace8205fc46cc4117877f","last_reissued_at":"2026-05-17T23:50:01.983876Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:01.983876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"C. S. George Lee, Debasmit Das","submitted_at":"2019-03-27T21:07:00Z","abstract_excerpt":"Zero-shot learning (ZSL) for image classification focuses on recognizing novel categories that have no labeled data available for training. The learning is generally carried out with the help of mid-level semantic descriptors associated with each class. This semantic-descriptor space is generally shared by both seen and unseen categories. However, ZSL suffers from hubness, domain discrepancy and biased-ness towards seen classes. To tackle these problems, we propose a three-step approach to zero-shot learning. Firstly, a mapping is learned from the semantic-descriptor space to the image-feature"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11701","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1903.11701","created_at":"2026-05-17T23:50:01.983938+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.11701v1","created_at":"2026-05-17T23:50:01.983938+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11701","created_at":"2026-05-17T23:50:01.983938+00:00"},{"alias_kind":"pith_short_12","alias_value":"35YU4KUCTYBJ","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"35YU4KUCTYBJ6W7I","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"35YU4KUC","created_at":"2026-05-18T12:33:07.085635+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/35YU4KUCTYBJ6W7I6BHTRPAGMI","json":"https://pith.science/pith/35YU4KUCTYBJ6W7I6BHTRPAGMI.json","graph_json":"https://pith.science/api/pith-number/35YU4KUCTYBJ6W7I6BHTRPAGMI/graph.json","events_json":"https://pith.science/api/pith-number/35YU4KUCTYBJ6W7I6BHTRPAGMI/events.json","paper":"https://pith.science/paper/35YU4KUC"},"agent_actions":{"view_html":"https://pith.science/pith/35YU4KUCTYBJ6W7I6BHTRPAGMI","download_json":"https://pith.science/pith/35YU4KUCTYBJ6W7I6BHTRPAGMI.json","view_paper":"https://pith.science/paper/35YU4KUC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.11701&json=true","fetch_graph":"https://pith.science/api/pith-number/35YU4KUCTYBJ6W7I6BHTRPAGMI/graph.json","fetch_events":"https://pith.science/api/pith-number/35YU4KUCTYBJ6W7I6BHTRPAGMI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/35YU4KUCTYBJ6W7I6BHTRPAGMI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/35YU4KUCTYBJ6W7I6BHTRPAGMI/action/storage_attestation","attest_author":"https://pith.science/pith/35YU4KUCTYBJ6W7I6BHTRPAGMI/action/author_attestation","sign_citation":"https://pith.science/pith/35YU4KUCTYBJ6W7I6BHTRPAGMI/action/citation_signature","submit_replication":"https://pith.science/pith/35YU4KUCTYBJ6W7I6BHTRPAGMI/action/replication_record"}},"created_at":"2026-05-17T23:50:01.983938+00:00","updated_at":"2026-05-17T23:50:01.983938+00:00"}