{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OR2U67VIYY7U6C4VWYAXHDQ5DP","short_pith_number":"pith:OR2U67VI","schema_version":"1.0","canonical_sha256":"74754f7ea8c63f4f0b95b601738e1d1beff5acf2fe7a47e719b339825c3b41ff","source":{"kind":"arxiv","id":"1709.01295","version":1},"attestation_state":"computed","paper":{"title":"SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Abhijat Biswas, Isht Dwivedi, Ravi Kiran Sarvadevabhatla, R. Venkatesh Babu, Sahil Manocha","submitted_at":"2017-09-05T09:10:59Z","abstract_excerpt":"The ability to semantically interpret hand-drawn line sketches, although very challenging, can pave way for novel applications in multimedia. We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches. SketchParse is configured as a two-level fully convolutional network. The first level contains shared layers common to all object categories. The second level contains a number of expert sub-networks. Each expert specializes in parsing sketches from object categories which contain structurally similar parts. Effectively, the two-level conf"},"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":"1709.01295","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-05T09:10:59Z","cross_cats_sorted":["cs.GR","cs.MM"],"title_canon_sha256":"d354ec14993911c8169f34a5b4133226a0a75cc5832f68e9f154f845261a25fb","abstract_canon_sha256":"a3f4e3c49b6824ea7beaba4a3b91a475a527e77951b4c5ec230bf171a6f6489c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:00.461793Z","signature_b64":"/wn10lJVaQHFilMCTQ5T4MP01KhFaFK+q6Od0yovwbhqddRcL8anRg4ElU+i4VjlfPoJhz2DuelRUiwOM28rCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74754f7ea8c63f4f0b95b601738e1d1beff5acf2fe7a47e719b339825c3b41ff","last_reissued_at":"2026-05-18T00:36:00.461194Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:00.461194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Abhijat Biswas, Isht Dwivedi, Ravi Kiran Sarvadevabhatla, R. Venkatesh Babu, Sahil Manocha","submitted_at":"2017-09-05T09:10:59Z","abstract_excerpt":"The ability to semantically interpret hand-drawn line sketches, although very challenging, can pave way for novel applications in multimedia. We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches. SketchParse is configured as a two-level fully convolutional network. The first level contains shared layers common to all object categories. The second level contains a number of expert sub-networks. Each expert specializes in parsing sketches from object categories which contain structurally similar parts. Effectively, the two-level conf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01295","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":"1709.01295","created_at":"2026-05-18T00:36:00.461282+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.01295v1","created_at":"2026-05-18T00:36:00.461282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01295","created_at":"2026-05-18T00:36:00.461282+00:00"},{"alias_kind":"pith_short_12","alias_value":"OR2U67VIYY7U","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OR2U67VIYY7U6C4V","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OR2U67VI","created_at":"2026-05-18T12:31:34.259226+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/OR2U67VIYY7U6C4VWYAXHDQ5DP","json":"https://pith.science/pith/OR2U67VIYY7U6C4VWYAXHDQ5DP.json","graph_json":"https://pith.science/api/pith-number/OR2U67VIYY7U6C4VWYAXHDQ5DP/graph.json","events_json":"https://pith.science/api/pith-number/OR2U67VIYY7U6C4VWYAXHDQ5DP/events.json","paper":"https://pith.science/paper/OR2U67VI"},"agent_actions":{"view_html":"https://pith.science/pith/OR2U67VIYY7U6C4VWYAXHDQ5DP","download_json":"https://pith.science/pith/OR2U67VIYY7U6C4VWYAXHDQ5DP.json","view_paper":"https://pith.science/paper/OR2U67VI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.01295&json=true","fetch_graph":"https://pith.science/api/pith-number/OR2U67VIYY7U6C4VWYAXHDQ5DP/graph.json","fetch_events":"https://pith.science/api/pith-number/OR2U67VIYY7U6C4VWYAXHDQ5DP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OR2U67VIYY7U6C4VWYAXHDQ5DP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OR2U67VIYY7U6C4VWYAXHDQ5DP/action/storage_attestation","attest_author":"https://pith.science/pith/OR2U67VIYY7U6C4VWYAXHDQ5DP/action/author_attestation","sign_citation":"https://pith.science/pith/OR2U67VIYY7U6C4VWYAXHDQ5DP/action/citation_signature","submit_replication":"https://pith.science/pith/OR2U67VIYY7U6C4VWYAXHDQ5DP/action/replication_record"}},"created_at":"2026-05-18T00:36:00.461282+00:00","updated_at":"2026-05-18T00:36:00.461282+00:00"}