{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:QROXT2W6ZA3TRUBIJXC27U7SNB","short_pith_number":"pith:QROXT2W6","schema_version":"1.0","canonical_sha256":"845d79eadec83738d0284dc5afd3f2684e16b0aff46412810f83f1456155e97b","source":{"kind":"arxiv","id":"1611.04246","version":2},"attestation_state":"computed","paper":{"title":"Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Quanshi Zhang, Ruiming Cao, Song-Chun Zhu, Ying Nian Wu","submitted_at":"2016-11-14T04:13:37Z","abstract_excerpt":"This paper proposes a learning strategy that extracts object-part concepts from a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually grow a semantically interpretable graphical model on the pre-trained CNN for hierarchical object understanding. Given part annotations on very few (e.g., 3-12) objects, our method mines certain latent patterns from the pre-trained CNN and associates them with different semantic parts. We use a four-layer And-Or graph to organize the mined latent patterns, so as to clarify their inter"},"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":"1611.04246","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-14T04:13:37Z","cross_cats_sorted":[],"title_canon_sha256":"7caf905435463726acd96c795c935f10e16185d4592750f0b7845f10ec62dc73","abstract_canon_sha256":"a5d0ab56022f970f6e62169fee2d354bf05a7352fdff801a358fa180c0d6eeeb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:53.718651Z","signature_b64":"KyUtjuMVHv2O9+ITt9jKfYitOCaI8nvCOFIew6HsCkKFbd6zHixk7YQ6J/AdIC1r6fvVgvjypXvAiJcAc1DzDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"845d79eadec83738d0284dc5afd3f2684e16b0aff46412810f83f1456155e97b","last_reissued_at":"2026-05-18T00:48:53.717899Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:53.717899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Quanshi Zhang, Ruiming Cao, Song-Chun Zhu, Ying Nian Wu","submitted_at":"2016-11-14T04:13:37Z","abstract_excerpt":"This paper proposes a learning strategy that extracts object-part concepts from a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually grow a semantically interpretable graphical model on the pre-trained CNN for hierarchical object understanding. Given part annotations on very few (e.g., 3-12) objects, our method mines certain latent patterns from the pre-trained CNN and associates them with different semantic parts. We use a four-layer And-Or graph to organize the mined latent patterns, so as to clarify their inter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.04246","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":""},"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":"1611.04246","created_at":"2026-05-18T00:48:53.718015+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.04246v2","created_at":"2026-05-18T00:48:53.718015+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.04246","created_at":"2026-05-18T00:48:53.718015+00:00"},{"alias_kind":"pith_short_12","alias_value":"QROXT2W6ZA3T","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"QROXT2W6ZA3TRUBI","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"QROXT2W6","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/QROXT2W6ZA3TRUBIJXC27U7SNB","json":"https://pith.science/pith/QROXT2W6ZA3TRUBIJXC27U7SNB.json","graph_json":"https://pith.science/api/pith-number/QROXT2W6ZA3TRUBIJXC27U7SNB/graph.json","events_json":"https://pith.science/api/pith-number/QROXT2W6ZA3TRUBIJXC27U7SNB/events.json","paper":"https://pith.science/paper/QROXT2W6"},"agent_actions":{"view_html":"https://pith.science/pith/QROXT2W6ZA3TRUBIJXC27U7SNB","download_json":"https://pith.science/pith/QROXT2W6ZA3TRUBIJXC27U7SNB.json","view_paper":"https://pith.science/paper/QROXT2W6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.04246&json=true","fetch_graph":"https://pith.science/api/pith-number/QROXT2W6ZA3TRUBIJXC27U7SNB/graph.json","fetch_events":"https://pith.science/api/pith-number/QROXT2W6ZA3TRUBIJXC27U7SNB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QROXT2W6ZA3TRUBIJXC27U7SNB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QROXT2W6ZA3TRUBIJXC27U7SNB/action/storage_attestation","attest_author":"https://pith.science/pith/QROXT2W6ZA3TRUBIJXC27U7SNB/action/author_attestation","sign_citation":"https://pith.science/pith/QROXT2W6ZA3TRUBIJXC27U7SNB/action/citation_signature","submit_replication":"https://pith.science/pith/QROXT2W6ZA3TRUBIJXC27U7SNB/action/replication_record"}},"created_at":"2026-05-18T00:48:53.718015+00:00","updated_at":"2026-05-18T00:48:53.718015+00:00"}