{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RIPDAHATKTZA6CI464SNLPD6A4","short_pith_number":"pith:RIPDAHAT","schema_version":"1.0","canonical_sha256":"8a1e301c1354f20f091cf724d5bc7e070391beccdc46abfba2fc8b5f6fc25f38","source":{"kind":"arxiv","id":"1605.06878","version":1},"attestation_state":"computed","paper":{"title":"Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen-Wei Xie, Jianxin Wu, Xiu-Shen Wei","submitted_at":"2016-05-23T02:46:47Z","abstract_excerpt":"Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations. In this paper, we propose a novel end-to-end Mask-CNN model without the fully connected layers for fine-grained recognition. Based on the part annotations of fine-grained images, the proposed model consists of a fully convolutional network to both locate the discriminative parts (e.g., head and torso), and more importantly generate object/part masks for selecting use"},"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":"1605.06878","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-05-23T02:46:47Z","cross_cats_sorted":[],"title_canon_sha256":"707d2f1ac03e9fb68c3f1a853467e3b75773941790526d96a8033bd7e15e774a","abstract_canon_sha256":"3e9a7071beb03114e0a25a28ce07ea0d341485fc76cc1cedec41a78ca2d7bbeb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:14:11.013657Z","signature_b64":"G6SZv6yxY1LYZXrGGaY4LA6yUZwCnk1ohjGVGZ2fYeZWxjGLkXWQhu+oPk8gfD22wmYkvLyAn/PHBQ07OrxBBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a1e301c1354f20f091cf724d5bc7e070391beccdc46abfba2fc8b5f6fc25f38","last_reissued_at":"2026-05-18T01:14:11.012965Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:14:11.012965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen-Wei Xie, Jianxin Wu, Xiu-Shen Wei","submitted_at":"2016-05-23T02:46:47Z","abstract_excerpt":"Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations. In this paper, we propose a novel end-to-end Mask-CNN model without the fully connected layers for fine-grained recognition. Based on the part annotations of fine-grained images, the proposed model consists of a fully convolutional network to both locate the discriminative parts (e.g., head and torso), and more importantly generate object/part masks for selecting use"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.06878","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":"1605.06878","created_at":"2026-05-18T01:14:11.013058+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.06878v1","created_at":"2026-05-18T01:14:11.013058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.06878","created_at":"2026-05-18T01:14:11.013058+00:00"},{"alias_kind":"pith_short_12","alias_value":"RIPDAHATKTZA","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RIPDAHATKTZA6CI4","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RIPDAHAT","created_at":"2026-05-18T12:30:41.710351+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/RIPDAHATKTZA6CI464SNLPD6A4","json":"https://pith.science/pith/RIPDAHATKTZA6CI464SNLPD6A4.json","graph_json":"https://pith.science/api/pith-number/RIPDAHATKTZA6CI464SNLPD6A4/graph.json","events_json":"https://pith.science/api/pith-number/RIPDAHATKTZA6CI464SNLPD6A4/events.json","paper":"https://pith.science/paper/RIPDAHAT"},"agent_actions":{"view_html":"https://pith.science/pith/RIPDAHATKTZA6CI464SNLPD6A4","download_json":"https://pith.science/pith/RIPDAHATKTZA6CI464SNLPD6A4.json","view_paper":"https://pith.science/paper/RIPDAHAT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.06878&json=true","fetch_graph":"https://pith.science/api/pith-number/RIPDAHATKTZA6CI464SNLPD6A4/graph.json","fetch_events":"https://pith.science/api/pith-number/RIPDAHATKTZA6CI464SNLPD6A4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RIPDAHATKTZA6CI464SNLPD6A4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RIPDAHATKTZA6CI464SNLPD6A4/action/storage_attestation","attest_author":"https://pith.science/pith/RIPDAHATKTZA6CI464SNLPD6A4/action/author_attestation","sign_citation":"https://pith.science/pith/RIPDAHATKTZA6CI464SNLPD6A4/action/citation_signature","submit_replication":"https://pith.science/pith/RIPDAHATKTZA6CI464SNLPD6A4/action/replication_record"}},"created_at":"2026-05-18T01:14:11.013058+00:00","updated_at":"2026-05-18T01:14:11.013058+00:00"}