{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CYSRGLB5P2CMSKWFL5BVOXVXKE","short_pith_number":"pith:CYSRGLB5","schema_version":"1.0","canonical_sha256":"1625132c3d7e84c92ac55f43575eb7513ad4249e0cdffedeea3212374b20bb0d","source":{"kind":"arxiv","id":"2407.01104","version":1},"attestation_state":"computed","paper":{"title":"Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenyu Dong, Chen Zhao, Weiling Cai, Ziqi Zeng","submitted_at":"2024-07-01T09:14:38Z","abstract_excerpt":"Existing unsupervised methods have addressed the challenges of inconsistent paired data and tedious acquisition of ground-truth labels in shadow removal tasks. However, GAN-based training often faces issues such as mode collapse and unstable optimization. Furthermore, due to the complex mapping between shadow and shadow-free domains, merely relying on adversarial learning is not enough to capture the underlying relationship between two domains, resulting in low quality of the generated images. To address these problems, we propose a semantic-guided adversarial diffusion framework for self-supe"},"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":"2407.01104","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-01T09:14:38Z","cross_cats_sorted":[],"title_canon_sha256":"5b8a4ca4f486c6fdb72832e8fbd9ec24c689735df1bde786b49e4bd9d2ac1924","abstract_canon_sha256":"f597a0fe3ad47a44a41bb3d7ca5a80dea3b361b57ca45f2beea0e6995c5c3a1b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:43.189419Z","signature_b64":"/aWaZQXqguwENmqp5HynbJrT0ANyGR7Q102hB/YGUiTjQjZQznvHhqJuRJtXot7E4Q5GBfse9R/XZkH7K4x4Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1625132c3d7e84c92ac55f43575eb7513ad4249e0cdffedeea3212374b20bb0d","last_reissued_at":"2026-07-05T08:38:43.189003Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:43.189003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenyu Dong, Chen Zhao, Weiling Cai, Ziqi Zeng","submitted_at":"2024-07-01T09:14:38Z","abstract_excerpt":"Existing unsupervised methods have addressed the challenges of inconsistent paired data and tedious acquisition of ground-truth labels in shadow removal tasks. However, GAN-based training often faces issues such as mode collapse and unstable optimization. Furthermore, due to the complex mapping between shadow and shadow-free domains, merely relying on adversarial learning is not enough to capture the underlying relationship between two domains, resulting in low quality of the generated images. To address these problems, we propose a semantic-guided adversarial diffusion framework for self-supe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01104","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/2407.01104/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":"2407.01104","created_at":"2026-07-05T08:38:43.189059+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.01104v1","created_at":"2026-07-05T08:38:43.189059+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01104","created_at":"2026-07-05T08:38:43.189059+00:00"},{"alias_kind":"pith_short_12","alias_value":"CYSRGLB5P2CM","created_at":"2026-07-05T08:38:43.189059+00:00"},{"alias_kind":"pith_short_16","alias_value":"CYSRGLB5P2CMSKWF","created_at":"2026-07-05T08:38:43.189059+00:00"},{"alias_kind":"pith_short_8","alias_value":"CYSRGLB5","created_at":"2026-07-05T08:38:43.189059+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/CYSRGLB5P2CMSKWFL5BVOXVXKE","json":"https://pith.science/pith/CYSRGLB5P2CMSKWFL5BVOXVXKE.json","graph_json":"https://pith.science/api/pith-number/CYSRGLB5P2CMSKWFL5BVOXVXKE/graph.json","events_json":"https://pith.science/api/pith-number/CYSRGLB5P2CMSKWFL5BVOXVXKE/events.json","paper":"https://pith.science/paper/CYSRGLB5"},"agent_actions":{"view_html":"https://pith.science/pith/CYSRGLB5P2CMSKWFL5BVOXVXKE","download_json":"https://pith.science/pith/CYSRGLB5P2CMSKWFL5BVOXVXKE.json","view_paper":"https://pith.science/paper/CYSRGLB5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.01104&json=true","fetch_graph":"https://pith.science/api/pith-number/CYSRGLB5P2CMSKWFL5BVOXVXKE/graph.json","fetch_events":"https://pith.science/api/pith-number/CYSRGLB5P2CMSKWFL5BVOXVXKE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CYSRGLB5P2CMSKWFL5BVOXVXKE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CYSRGLB5P2CMSKWFL5BVOXVXKE/action/storage_attestation","attest_author":"https://pith.science/pith/CYSRGLB5P2CMSKWFL5BVOXVXKE/action/author_attestation","sign_citation":"https://pith.science/pith/CYSRGLB5P2CMSKWFL5BVOXVXKE/action/citation_signature","submit_replication":"https://pith.science/pith/CYSRGLB5P2CMSKWFL5BVOXVXKE/action/replication_record"}},"created_at":"2026-07-05T08:38:43.189059+00:00","updated_at":"2026-07-05T08:38:43.189059+00:00"}