{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:QRZMBVF4KDCUW6E6DF75L5N2PY","short_pith_number":"pith:QRZMBVF4","schema_version":"1.0","canonical_sha256":"8472c0d4bc50c54b789e197fd5f5ba7e3391941f833a3b3ef61427427f927efb","source":{"kind":"arxiv","id":"1710.04837","version":1},"attestation_state":"computed","paper":{"title":"Recent Advances in Zero-shot Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM","stat.ML"],"primary_cat":"cs.CV","authors_text":"Leonid Sigal, Shaogang Gong, Tao Xiang, Xiangyang Xue, Yanwei Fu, Yu-Gang Jiang","submitted_at":"2017-10-13T08:29:29Z","abstract_excerpt":"With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing ze"},"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":"1710.04837","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-13T08:29:29Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MM","stat.ML"],"title_canon_sha256":"86ba7944035cc5c1f9d86c92fd5d17caf0a243d2d723a64cdbacc2dd2e822be8","abstract_canon_sha256":"80f181b3a4afb9c7abf8dee5d13be8c03944f7a3ffcfa544042a3e86060ecdb3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:56.558201Z","signature_b64":"Tu4EmpDjNBsM90d/7TeLvrHpJf5SAu79zM6kb49jxx2esanFVtJ8GQqcmdslhKn0AM3UUFsvxW55K6XiQwJsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8472c0d4bc50c54b789e197fd5f5ba7e3391941f833a3b3ef61427427f927efb","last_reissued_at":"2026-05-18T00:32:56.557631Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:56.557631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recent Advances in Zero-shot Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM","stat.ML"],"primary_cat":"cs.CV","authors_text":"Leonid Sigal, Shaogang Gong, Tao Xiang, Xiangyang Xue, Yanwei Fu, Yu-Gang Jiang","submitted_at":"2017-10-13T08:29:29Z","abstract_excerpt":"With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing ze"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04837","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":"1710.04837","created_at":"2026-05-18T00:32:56.557720+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.04837v1","created_at":"2026-05-18T00:32:56.557720+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04837","created_at":"2026-05-18T00:32:56.557720+00:00"},{"alias_kind":"pith_short_12","alias_value":"QRZMBVF4KDCU","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"QRZMBVF4KDCUW6E6","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"QRZMBVF4","created_at":"2026-05-18T12:31:39.905425+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.08070","citing_title":"Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY","json":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY.json","graph_json":"https://pith.science/api/pith-number/QRZMBVF4KDCUW6E6DF75L5N2PY/graph.json","events_json":"https://pith.science/api/pith-number/QRZMBVF4KDCUW6E6DF75L5N2PY/events.json","paper":"https://pith.science/paper/QRZMBVF4"},"agent_actions":{"view_html":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY","download_json":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY.json","view_paper":"https://pith.science/paper/QRZMBVF4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.04837&json=true","fetch_graph":"https://pith.science/api/pith-number/QRZMBVF4KDCUW6E6DF75L5N2PY/graph.json","fetch_events":"https://pith.science/api/pith-number/QRZMBVF4KDCUW6E6DF75L5N2PY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY/action/storage_attestation","attest_author":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY/action/author_attestation","sign_citation":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY/action/citation_signature","submit_replication":"https://pith.science/pith/QRZMBVF4KDCUW6E6DF75L5N2PY/action/replication_record"}},"created_at":"2026-05-18T00:32:56.557720+00:00","updated_at":"2026-05-18T00:32:56.557720+00:00"}