{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:BI6F26EBHBIMJGIDGY5YTBBJNI","short_pith_number":"pith:BI6F26EB","schema_version":"1.0","canonical_sha256":"0a3c5d78813850c49903363b8984296a0796791ce9e2ceb0180d9f4b3eb9941e","source":{"kind":"arxiv","id":"2203.03137","version":2},"attestation_state":"computed","paper":{"title":"MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guo-Sen Xie, Jian Zhao, Kai Wang, Qinmu Peng, Shiming Chen, Wenhan Yang, Xinge You, Ziming Hong","submitted_at":"2022-03-07T05:27:08Z","abstract_excerpt":"The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus achieving a desirable knowledge transfer to unseen classes. Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e.g., attribute semantics) between visual and attribute features. To solve the above dilemma, we propose a Mutually Se"},"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":"2203.03137","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-07T05:27:08Z","cross_cats_sorted":[],"title_canon_sha256":"6207759c0a41d90a72cd81ae716bcf01a412631dee0592fa2cc9d3df73275bf9","abstract_canon_sha256":"deadf9b1b48379ca867fa5a732b6bd8ae2692764d3059198f7653b67c54b66f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:16:54.759642Z","signature_b64":"b7OjHfW747SJNK8x860JHTuIlcl9fvXwJhoL12kmG+rW7J0qGmHPC0TVibCoZJSyzjpsHik5LKXIihTzUJ9IBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a3c5d78813850c49903363b8984296a0796791ce9e2ceb0180d9f4b3eb9941e","last_reissued_at":"2026-07-05T04:16:54.759098Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:16:54.759098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guo-Sen Xie, Jian Zhao, Kai Wang, Qinmu Peng, Shiming Chen, Wenhan Yang, Xinge You, Ziming Hong","submitted_at":"2022-03-07T05:27:08Z","abstract_excerpt":"The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus achieving a desirable knowledge transfer to unseen classes. Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e.g., attribute semantics) between visual and attribute features. To solve the above dilemma, we propose a Mutually Se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.03137","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.03137/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2203.03137","created_at":"2026-07-05T04:16:54.759155+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.03137v2","created_at":"2026-07-05T04:16:54.759155+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.03137","created_at":"2026-07-05T04:16:54.759155+00:00"},{"alias_kind":"pith_short_12","alias_value":"BI6F26EBHBIM","created_at":"2026-07-05T04:16:54.759155+00:00"},{"alias_kind":"pith_short_16","alias_value":"BI6F26EBHBIMJGID","created_at":"2026-07-05T04:16:54.759155+00:00"},{"alias_kind":"pith_short_8","alias_value":"BI6F26EB","created_at":"2026-07-05T04:16:54.759155+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/BI6F26EBHBIMJGIDGY5YTBBJNI","json":"https://pith.science/pith/BI6F26EBHBIMJGIDGY5YTBBJNI.json","graph_json":"https://pith.science/api/pith-number/BI6F26EBHBIMJGIDGY5YTBBJNI/graph.json","events_json":"https://pith.science/api/pith-number/BI6F26EBHBIMJGIDGY5YTBBJNI/events.json","paper":"https://pith.science/paper/BI6F26EB"},"agent_actions":{"view_html":"https://pith.science/pith/BI6F26EBHBIMJGIDGY5YTBBJNI","download_json":"https://pith.science/pith/BI6F26EBHBIMJGIDGY5YTBBJNI.json","view_paper":"https://pith.science/paper/BI6F26EB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.03137&json=true","fetch_graph":"https://pith.science/api/pith-number/BI6F26EBHBIMJGIDGY5YTBBJNI/graph.json","fetch_events":"https://pith.science/api/pith-number/BI6F26EBHBIMJGIDGY5YTBBJNI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BI6F26EBHBIMJGIDGY5YTBBJNI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BI6F26EBHBIMJGIDGY5YTBBJNI/action/storage_attestation","attest_author":"https://pith.science/pith/BI6F26EBHBIMJGIDGY5YTBBJNI/action/author_attestation","sign_citation":"https://pith.science/pith/BI6F26EBHBIMJGIDGY5YTBBJNI/action/citation_signature","submit_replication":"https://pith.science/pith/BI6F26EBHBIMJGIDGY5YTBBJNI/action/replication_record"}},"created_at":"2026-07-05T04:16:54.759155+00:00","updated_at":"2026-07-05T04:16:54.759155+00:00"}