{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:43V6AHVSF2PWS3IPTZLNAXIY7A","short_pith_number":"pith:43V6AHVS","schema_version":"1.0","canonical_sha256":"e6ebe01eb22e9f696d0f9e56d05d18f836506ebaf2dc5deb30d7543302db95e6","source":{"kind":"arxiv","id":"1712.02478","version":1},"attestation_state":"computed","paper":{"title":"Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian Yang, Jifeng Wang, Le Hui, Xiang Li","submitted_at":"2017-12-07T02:57:38Z","abstract_excerpt":"Understanding shadows from a single image spontaneously derives into two types of task in previous studies, containing shadow detection and shadow removal. In this paper, we present a multi-task perspective, which is not embraced by any existing work, to jointly learn both detection and removal in an end-to-end fashion that aims at enjoying the mutually improved benefits from each other. Our framework is based on a novel STacked Conditional Generative Adversarial Network (ST-CGAN), which is composed of two stacked CGANs, each with a generator and a discriminator. Specifically, a shadow image i"},"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":"1712.02478","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-07T02:57:38Z","cross_cats_sorted":[],"title_canon_sha256":"91868592daba5fb42d5119a9359fea5fd7d422750cef67dad75abe348d235d3d","abstract_canon_sha256":"2332be53360c118844ed1d20a48fccf43dab3d100f48696d21c4b08791888a93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:33.958656Z","signature_b64":"Bs4/JTAOsTiMvEP5S0A3PjxYnruCp9Vg+iqKp7G6fc/p4Zh+lVAFitkL73ScCRLLphTsEYnl56Bi4TSOGLDYCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6ebe01eb22e9f696d0f9e56d05d18f836506ebaf2dc5deb30d7543302db95e6","last_reissued_at":"2026-05-18T00:28:33.957737Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:33.957737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian Yang, Jifeng Wang, Le Hui, Xiang Li","submitted_at":"2017-12-07T02:57:38Z","abstract_excerpt":"Understanding shadows from a single image spontaneously derives into two types of task in previous studies, containing shadow detection and shadow removal. In this paper, we present a multi-task perspective, which is not embraced by any existing work, to jointly learn both detection and removal in an end-to-end fashion that aims at enjoying the mutually improved benefits from each other. Our framework is based on a novel STacked Conditional Generative Adversarial Network (ST-CGAN), which is composed of two stacked CGANs, each with a generator and a discriminator. Specifically, a shadow image i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.02478","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":"1712.02478","created_at":"2026-05-18T00:28:33.957881+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.02478v1","created_at":"2026-05-18T00:28:33.957881+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.02478","created_at":"2026-05-18T00:28:33.957881+00:00"},{"alias_kind":"pith_short_12","alias_value":"43V6AHVSF2PW","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"43V6AHVSF2PWS3IP","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"43V6AHVS","created_at":"2026-05-18T12:30:58.224056+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/43V6AHVSF2PWS3IPTZLNAXIY7A","json":"https://pith.science/pith/43V6AHVSF2PWS3IPTZLNAXIY7A.json","graph_json":"https://pith.science/api/pith-number/43V6AHVSF2PWS3IPTZLNAXIY7A/graph.json","events_json":"https://pith.science/api/pith-number/43V6AHVSF2PWS3IPTZLNAXIY7A/events.json","paper":"https://pith.science/paper/43V6AHVS"},"agent_actions":{"view_html":"https://pith.science/pith/43V6AHVSF2PWS3IPTZLNAXIY7A","download_json":"https://pith.science/pith/43V6AHVSF2PWS3IPTZLNAXIY7A.json","view_paper":"https://pith.science/paper/43V6AHVS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.02478&json=true","fetch_graph":"https://pith.science/api/pith-number/43V6AHVSF2PWS3IPTZLNAXIY7A/graph.json","fetch_events":"https://pith.science/api/pith-number/43V6AHVSF2PWS3IPTZLNAXIY7A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/43V6AHVSF2PWS3IPTZLNAXIY7A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/43V6AHVSF2PWS3IPTZLNAXIY7A/action/storage_attestation","attest_author":"https://pith.science/pith/43V6AHVSF2PWS3IPTZLNAXIY7A/action/author_attestation","sign_citation":"https://pith.science/pith/43V6AHVSF2PWS3IPTZLNAXIY7A/action/citation_signature","submit_replication":"https://pith.science/pith/43V6AHVSF2PWS3IPTZLNAXIY7A/action/replication_record"}},"created_at":"2026-05-18T00:28:33.957881+00:00","updated_at":"2026-05-18T00:28:33.957881+00:00"}