{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:XUP5YO5OOVQVFJITYFCGH6K3E7","short_pith_number":"pith:XUP5YO5O","schema_version":"1.0","canonical_sha256":"bd1fdc3bae756152a513c14463f95b27cc739b68873e596961d775f124e4f14c","source":{"kind":"arxiv","id":"2109.07755","version":1},"attestation_state":"computed","paper":{"title":"Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Miaohua Zhang, Xiaohan Yu, Yongsheng Gao, Zicheng Pan","submitted_at":"2021-09-16T06:57:05Z","abstract_excerpt":"While the fine-grained visual categorization (FGVC) problems have been greatly developed in the past years, the Ultra-fine-grained visual categorization (Ultra-FGVC) problems have been understudied. FGVC aims at classifying objects from the same species (very similar categories), while the Ultra-FGVC targets at more challenging problems of classifying images at an ultra-fine granularity where even human experts may fail to identify the visual difference. The challenges for Ultra-FGVC mainly comes from two aspects: one is that the Ultra-FGVC often arises overfitting problems due to the lack of "},"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":"2109.07755","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T06:57:05Z","cross_cats_sorted":[],"title_canon_sha256":"b94438c0ed38584974be43abb84e5a71fdfeafe9c5c63b3c40c1985757fbea83","abstract_canon_sha256":"ea3afb9997875fed40c36bcb0bae11c7ba1c3fac15f1a0b680b5161d78195e12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:15:00.781386Z","signature_b64":"S/qnA0EaKSXltD5DvcVgDMcv4EOfAF0ALpG5lYhzvT6ykn5wZ8RnPcCixObgiSBMp9Q75CIwzWiSTAAT8KgmCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd1fdc3bae756152a513c14463f95b27cc739b68873e596961d775f124e4f14c","last_reissued_at":"2026-07-05T03:15:00.780928Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:15:00.780928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Miaohua Zhang, Xiaohan Yu, Yongsheng Gao, Zicheng Pan","submitted_at":"2021-09-16T06:57:05Z","abstract_excerpt":"While the fine-grained visual categorization (FGVC) problems have been greatly developed in the past years, the Ultra-fine-grained visual categorization (Ultra-FGVC) problems have been understudied. FGVC aims at classifying objects from the same species (very similar categories), while the Ultra-FGVC targets at more challenging problems of classifying images at an ultra-fine granularity where even human experts may fail to identify the visual difference. The challenges for Ultra-FGVC mainly comes from two aspects: one is that the Ultra-FGVC often arises overfitting problems due to the lack of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.07755","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.07755/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":"2109.07755","created_at":"2026-07-05T03:15:00.780985+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.07755v1","created_at":"2026-07-05T03:15:00.780985+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.07755","created_at":"2026-07-05T03:15:00.780985+00:00"},{"alias_kind":"pith_short_12","alias_value":"XUP5YO5OOVQV","created_at":"2026-07-05T03:15:00.780985+00:00"},{"alias_kind":"pith_short_16","alias_value":"XUP5YO5OOVQVFJIT","created_at":"2026-07-05T03:15:00.780985+00:00"},{"alias_kind":"pith_short_8","alias_value":"XUP5YO5O","created_at":"2026-07-05T03:15:00.780985+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/XUP5YO5OOVQVFJITYFCGH6K3E7","json":"https://pith.science/pith/XUP5YO5OOVQVFJITYFCGH6K3E7.json","graph_json":"https://pith.science/api/pith-number/XUP5YO5OOVQVFJITYFCGH6K3E7/graph.json","events_json":"https://pith.science/api/pith-number/XUP5YO5OOVQVFJITYFCGH6K3E7/events.json","paper":"https://pith.science/paper/XUP5YO5O"},"agent_actions":{"view_html":"https://pith.science/pith/XUP5YO5OOVQVFJITYFCGH6K3E7","download_json":"https://pith.science/pith/XUP5YO5OOVQVFJITYFCGH6K3E7.json","view_paper":"https://pith.science/paper/XUP5YO5O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.07755&json=true","fetch_graph":"https://pith.science/api/pith-number/XUP5YO5OOVQVFJITYFCGH6K3E7/graph.json","fetch_events":"https://pith.science/api/pith-number/XUP5YO5OOVQVFJITYFCGH6K3E7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XUP5YO5OOVQVFJITYFCGH6K3E7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XUP5YO5OOVQVFJITYFCGH6K3E7/action/storage_attestation","attest_author":"https://pith.science/pith/XUP5YO5OOVQVFJITYFCGH6K3E7/action/author_attestation","sign_citation":"https://pith.science/pith/XUP5YO5OOVQVFJITYFCGH6K3E7/action/citation_signature","submit_replication":"https://pith.science/pith/XUP5YO5OOVQVFJITYFCGH6K3E7/action/replication_record"}},"created_at":"2026-07-05T03:15:00.780985+00:00","updated_at":"2026-07-05T03:15:00.780985+00:00"}