{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:IGM55UZFFRX3LM5J3CEXGCMQGN","short_pith_number":"pith:IGM55UZF","schema_version":"1.0","canonical_sha256":"4199ded3252c6fb5b3a9d88973099033668302ed517f889295cebbb9f18b0b42","source":{"kind":"arxiv","id":"1705.02429","version":1},"attestation_state":"computed","paper":{"title":"Deep Patch Learning for Weakly Supervised Object Classification and Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Peng Tang, Wenyu Liu, Xiang Bai, Xinggang Wang, Zilong Huang","submitted_at":"2017-05-06T02:05:38Z","abstract_excerpt":"Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background. Many existing methods usually require fine-grained supervisions (e.g., bounding-box annotations) to learn patch features, which requires a great effort to label images may limit their potential applications. In this paper, we propose to learn patch features via weak supervisions, i.e., only image-level supervisions. To achieve this goal, we treat images as bags and patches as instances to integrate the weakly superv"},"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":"1705.02429","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-06T02:05:38Z","cross_cats_sorted":[],"title_canon_sha256":"36212c8a6b6dd6102c6194368f59e4d533e65cd9bf15e8827cb7e5b14dbd11bc","abstract_canon_sha256":"b259c853623bd806d96d8a1fdcbb20d56b05c544ecf1f9618c2f02047a0d4680"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:56.573989Z","signature_b64":"tRQlWXXQpBW/9anM3r5pSvCiJ0ISyayUmgD5+8L7j40BokeqZCywJa7gej0pvsuTdJ0OmhypFZ4LTV/+H+P3Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4199ded3252c6fb5b3a9d88973099033668302ed517f889295cebbb9f18b0b42","last_reissued_at":"2026-05-18T00:44:56.573331Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:56.573331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Patch Learning for Weakly Supervised Object Classification and Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Peng Tang, Wenyu Liu, Xiang Bai, Xinggang Wang, Zilong Huang","submitted_at":"2017-05-06T02:05:38Z","abstract_excerpt":"Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background. Many existing methods usually require fine-grained supervisions (e.g., bounding-box annotations) to learn patch features, which requires a great effort to label images may limit their potential applications. In this paper, we propose to learn patch features via weak supervisions, i.e., only image-level supervisions. To achieve this goal, we treat images as bags and patches as instances to integrate the weakly superv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02429","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":"1705.02429","created_at":"2026-05-18T00:44:56.573442+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.02429v1","created_at":"2026-05-18T00:44:56.573442+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02429","created_at":"2026-05-18T00:44:56.573442+00:00"},{"alias_kind":"pith_short_12","alias_value":"IGM55UZFFRX3","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"IGM55UZFFRX3LM5J","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"IGM55UZF","created_at":"2026-05-18T12:31:21.493067+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/IGM55UZFFRX3LM5J3CEXGCMQGN","json":"https://pith.science/pith/IGM55UZFFRX3LM5J3CEXGCMQGN.json","graph_json":"https://pith.science/api/pith-number/IGM55UZFFRX3LM5J3CEXGCMQGN/graph.json","events_json":"https://pith.science/api/pith-number/IGM55UZFFRX3LM5J3CEXGCMQGN/events.json","paper":"https://pith.science/paper/IGM55UZF"},"agent_actions":{"view_html":"https://pith.science/pith/IGM55UZFFRX3LM5J3CEXGCMQGN","download_json":"https://pith.science/pith/IGM55UZFFRX3LM5J3CEXGCMQGN.json","view_paper":"https://pith.science/paper/IGM55UZF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.02429&json=true","fetch_graph":"https://pith.science/api/pith-number/IGM55UZFFRX3LM5J3CEXGCMQGN/graph.json","fetch_events":"https://pith.science/api/pith-number/IGM55UZFFRX3LM5J3CEXGCMQGN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IGM55UZFFRX3LM5J3CEXGCMQGN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IGM55UZFFRX3LM5J3CEXGCMQGN/action/storage_attestation","attest_author":"https://pith.science/pith/IGM55UZFFRX3LM5J3CEXGCMQGN/action/author_attestation","sign_citation":"https://pith.science/pith/IGM55UZFFRX3LM5J3CEXGCMQGN/action/citation_signature","submit_replication":"https://pith.science/pith/IGM55UZFFRX3LM5J3CEXGCMQGN/action/replication_record"}},"created_at":"2026-05-18T00:44:56.573442+00:00","updated_at":"2026-05-18T00:44:56.573442+00:00"}