{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OQ7WBRUWDXTUMLD46CAKBAN775","short_pith_number":"pith:OQ7WBRUW","schema_version":"1.0","canonical_sha256":"743f60c6961de7462c7cf080a081bfff6302dfbb32a4f60bca70fca29cb8d22f","source":{"kind":"arxiv","id":"2303.14968","version":1},"attestation_state":"computed","paper":{"title":"Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Guangtao Zhai, Kede Ma, Weixia Zhang, Xiaokang Yang, Ying Wei","submitted_at":"2023-03-27T07:58:09Z","abstract_excerpt":"We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically. Specifically, we first describe all candidate label combinations (from multiple tasks) using a textual template, and compute the joint probability from the cosine similarities of the visual-textual embeddings. Predictions of each task can "},"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":"2303.14968","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-27T07:58:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"de7ace862d64cde1f8ac1f907643ced6dd5cf105add05d9b6d6b9e909404089d","abstract_canon_sha256":"13180941ee03a37bfbf6c853a351d197e9e295af307a3ff0c0c1fee90f4d5ca0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:54:57.783078Z","signature_b64":"bR+QUZBSxOViHUpYQUtsRhiRlXgzj3C+9I809WGMyz6e01FI7yzpcOGDVIWnQjgCgbi0KzwbCH+nJUQU3yPsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"743f60c6961de7462c7cf080a081bfff6302dfbb32a4f60bca70fca29cb8d22f","last_reissued_at":"2026-07-05T05:54:57.782616Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:54:57.782616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Guangtao Zhai, Kede Ma, Weixia Zhang, Xiaokang Yang, Ying Wei","submitted_at":"2023-03-27T07:58:09Z","abstract_excerpt":"We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically. Specifically, we first describe all candidate label combinations (from multiple tasks) using a textual template, and compute the joint probability from the cosine similarities of the visual-textual embeddings. Predictions of each task can "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.14968","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/2303.14968/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":"2303.14968","created_at":"2026-07-05T05:54:57.782673+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.14968v1","created_at":"2026-07-05T05:54:57.782673+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.14968","created_at":"2026-07-05T05:54:57.782673+00:00"},{"alias_kind":"pith_short_12","alias_value":"OQ7WBRUWDXTU","created_at":"2026-07-05T05:54:57.782673+00:00"},{"alias_kind":"pith_short_16","alias_value":"OQ7WBRUWDXTUMLD4","created_at":"2026-07-05T05:54:57.782673+00:00"},{"alias_kind":"pith_short_8","alias_value":"OQ7WBRUW","created_at":"2026-07-05T05:54:57.782673+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.03494","citing_title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775","json":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775.json","graph_json":"https://pith.science/api/pith-number/OQ7WBRUWDXTUMLD46CAKBAN775/graph.json","events_json":"https://pith.science/api/pith-number/OQ7WBRUWDXTUMLD46CAKBAN775/events.json","paper":"https://pith.science/paper/OQ7WBRUW"},"agent_actions":{"view_html":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775","download_json":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775.json","view_paper":"https://pith.science/paper/OQ7WBRUW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.14968&json=true","fetch_graph":"https://pith.science/api/pith-number/OQ7WBRUWDXTUMLD46CAKBAN775/graph.json","fetch_events":"https://pith.science/api/pith-number/OQ7WBRUWDXTUMLD46CAKBAN775/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775/action/storage_attestation","attest_author":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775/action/author_attestation","sign_citation":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775/action/citation_signature","submit_replication":"https://pith.science/pith/OQ7WBRUWDXTUMLD46CAKBAN775/action/replication_record"}},"created_at":"2026-07-05T05:54:57.782673+00:00","updated_at":"2026-07-05T05:54:57.782673+00:00"}