{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:57R7HQ2UDW4FC6XUNF5QRKQKSZ","short_pith_number":"pith:57R7HQ2U","schema_version":"1.0","canonical_sha256":"efe3f3c3541db8517af4697b08aa0a964a4c6b4e8c6835d7848b745318c453f3","source":{"kind":"arxiv","id":"1704.01250","version":1},"attestation_state":"computed","paper":{"title":"Relative Learning from Web Images for Content-adaptive Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baoxin Li, Parag S. Chandakkar, Qiongjie Tian","submitted_at":"2017-04-05T03:13:01Z","abstract_excerpt":"Personalized and content-adaptive image enhancement can find many applications in the age of social media and mobile computing. This paper presents a relative-learning-based approach, which, unlike previous methods, does not require matching original and enhanced images for training. This allows the use of massive online photo collections to train a ranking model for improved enhancement. We first propose a multi-level ranking model, which is learned from only relatively-labeled inputs that are automatically crawled. Then we design a novel parameter sampling scheme under this model to generate"},"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":"1704.01250","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-05T03:13:01Z","cross_cats_sorted":[],"title_canon_sha256":"8b8229049de52160a2dc680935299efc6998384b00916eaff2904ba2fbf3e9c9","abstract_canon_sha256":"38f02439a87d75f2d1055f007406afda7e4e7493bc9dbe2fc65f78491956439f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:58.674216Z","signature_b64":"+TR/MMYz81Mvf9e2nmtwAQ6ezgo0dG+Ut+WwXB+ixpQw1t7GmDeoyVx2oWVW2GAD6nwIgqESZhYU0TU3TxiQCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efe3f3c3541db8517af4697b08aa0a964a4c6b4e8c6835d7848b745318c453f3","last_reissued_at":"2026-05-18T00:46:58.673545Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:58.673545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Relative Learning from Web Images for Content-adaptive Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baoxin Li, Parag S. Chandakkar, Qiongjie Tian","submitted_at":"2017-04-05T03:13:01Z","abstract_excerpt":"Personalized and content-adaptive image enhancement can find many applications in the age of social media and mobile computing. This paper presents a relative-learning-based approach, which, unlike previous methods, does not require matching original and enhanced images for training. This allows the use of massive online photo collections to train a ranking model for improved enhancement. We first propose a multi-level ranking model, which is learned from only relatively-labeled inputs that are automatically crawled. Then we design a novel parameter sampling scheme under this model to generate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.01250","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":"1704.01250","created_at":"2026-05-18T00:46:58.673651+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.01250v1","created_at":"2026-05-18T00:46:58.673651+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.01250","created_at":"2026-05-18T00:46:58.673651+00:00"},{"alias_kind":"pith_short_12","alias_value":"57R7HQ2UDW4F","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"57R7HQ2UDW4FC6XU","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"57R7HQ2U","created_at":"2026-05-18T12:31:00.734936+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/57R7HQ2UDW4FC6XUNF5QRKQKSZ","json":"https://pith.science/pith/57R7HQ2UDW4FC6XUNF5QRKQKSZ.json","graph_json":"https://pith.science/api/pith-number/57R7HQ2UDW4FC6XUNF5QRKQKSZ/graph.json","events_json":"https://pith.science/api/pith-number/57R7HQ2UDW4FC6XUNF5QRKQKSZ/events.json","paper":"https://pith.science/paper/57R7HQ2U"},"agent_actions":{"view_html":"https://pith.science/pith/57R7HQ2UDW4FC6XUNF5QRKQKSZ","download_json":"https://pith.science/pith/57R7HQ2UDW4FC6XUNF5QRKQKSZ.json","view_paper":"https://pith.science/paper/57R7HQ2U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.01250&json=true","fetch_graph":"https://pith.science/api/pith-number/57R7HQ2UDW4FC6XUNF5QRKQKSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/57R7HQ2UDW4FC6XUNF5QRKQKSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57R7HQ2UDW4FC6XUNF5QRKQKSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57R7HQ2UDW4FC6XUNF5QRKQKSZ/action/storage_attestation","attest_author":"https://pith.science/pith/57R7HQ2UDW4FC6XUNF5QRKQKSZ/action/author_attestation","sign_citation":"https://pith.science/pith/57R7HQ2UDW4FC6XUNF5QRKQKSZ/action/citation_signature","submit_replication":"https://pith.science/pith/57R7HQ2UDW4FC6XUNF5QRKQKSZ/action/replication_record"}},"created_at":"2026-05-18T00:46:58.673651+00:00","updated_at":"2026-05-18T00:46:58.673651+00:00"}