{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:GFMPHPEOZXJLKJNRTAMNNKHS5J","short_pith_number":"pith:GFMPHPEO","schema_version":"1.0","canonical_sha256":"3158f3bc8ecdd2b525b19818d6a8f2ea42e7f043eef7bbc016cefac8d618afdb","source":{"kind":"arxiv","id":"1709.01148","version":1},"attestation_state":"computed","paper":{"title":"Link the head to the \"beak\": Zero Shot Learning from Noisy Text Description at Part Precision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Elgammal, Han Zhang, Mohamed Elhoseiny, Yizhe Zhu","submitted_at":"2017-09-04T20:36:14Z","abstract_excerpt":"In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. For instance, this learning process enables terms like \"beak\" to be sparsely linked to the visual representation of parts like head, while reduces the effect of non-visual terms like \"migrate\" on classifier prediction. Images are encoded by a part-based CNN that detect bird parts and learn part"},"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":"1709.01148","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-04T20:36:14Z","cross_cats_sorted":[],"title_canon_sha256":"9913d8801118ca737462dd360275110280fc5cbc48e21bc56b155ffba3052466","abstract_canon_sha256":"e685ff90e98786ac147e56a7ee08521f2bef4f060b5a8d1df7b528e9b6bbc4e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:00.771420Z","signature_b64":"Sl6YK9ZDdF7do1My9V536OqxkmBjFtKdQBu1WPbcaUoxJ2vZhu/2ctm77nxldzRBki+a1UFbYe4FYxnUlbcrAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3158f3bc8ecdd2b525b19818d6a8f2ea42e7f043eef7bbc016cefac8d618afdb","last_reissued_at":"2026-05-18T00:36:00.770938Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:00.770938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Link the head to the \"beak\": Zero Shot Learning from Noisy Text Description at Part Precision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Elgammal, Han Zhang, Mohamed Elhoseiny, Yizhe Zhu","submitted_at":"2017-09-04T20:36:14Z","abstract_excerpt":"In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. For instance, this learning process enables terms like \"beak\" to be sparsely linked to the visual representation of parts like head, while reduces the effect of non-visual terms like \"migrate\" on classifier prediction. Images are encoded by a part-based CNN that detect bird parts and learn part"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01148","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":"1709.01148","created_at":"2026-05-18T00:36:00.771012+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.01148v1","created_at":"2026-05-18T00:36:00.771012+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01148","created_at":"2026-05-18T00:36:00.771012+00:00"},{"alias_kind":"pith_short_12","alias_value":"GFMPHPEOZXJL","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"GFMPHPEOZXJLKJNR","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"GFMPHPEO","created_at":"2026-05-18T12:31:15.632608+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/GFMPHPEOZXJLKJNRTAMNNKHS5J","json":"https://pith.science/pith/GFMPHPEOZXJLKJNRTAMNNKHS5J.json","graph_json":"https://pith.science/api/pith-number/GFMPHPEOZXJLKJNRTAMNNKHS5J/graph.json","events_json":"https://pith.science/api/pith-number/GFMPHPEOZXJLKJNRTAMNNKHS5J/events.json","paper":"https://pith.science/paper/GFMPHPEO"},"agent_actions":{"view_html":"https://pith.science/pith/GFMPHPEOZXJLKJNRTAMNNKHS5J","download_json":"https://pith.science/pith/GFMPHPEOZXJLKJNRTAMNNKHS5J.json","view_paper":"https://pith.science/paper/GFMPHPEO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.01148&json=true","fetch_graph":"https://pith.science/api/pith-number/GFMPHPEOZXJLKJNRTAMNNKHS5J/graph.json","fetch_events":"https://pith.science/api/pith-number/GFMPHPEOZXJLKJNRTAMNNKHS5J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GFMPHPEOZXJLKJNRTAMNNKHS5J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GFMPHPEOZXJLKJNRTAMNNKHS5J/action/storage_attestation","attest_author":"https://pith.science/pith/GFMPHPEOZXJLKJNRTAMNNKHS5J/action/author_attestation","sign_citation":"https://pith.science/pith/GFMPHPEOZXJLKJNRTAMNNKHS5J/action/citation_signature","submit_replication":"https://pith.science/pith/GFMPHPEOZXJLKJNRTAMNNKHS5J/action/replication_record"}},"created_at":"2026-05-18T00:36:00.771012+00:00","updated_at":"2026-05-18T00:36:00.771012+00:00"}