{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/msu-sr-qa-dataset/papers/ran/1","list_of":"/dataset/msu-sr-qa-dataset","dataset":"MSU SR-QA Dataset","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,10],"of":10,"counts":{"papers_with_a_benchmark_row":26,"with_a_code_link":23,"where_syntology_ran_a_sample":10,"not_listed_spam_title":0,"listed":26,"listed_where_code_ran":10,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":8,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":8,"listed_every_run_a_failure_of_syntologys_instrument":2,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/msu-sr-qa-dataset/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/q-align-teaching-lmms-for-visual-scoring-via","slug":"q-align-teaching-lmms-for-visual-scoring-via","title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","date":"2023-12-28","arxiv_id":"2312.17090","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["q-future/q-align"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/q-align-teaching-lmms-for-visual-scoring-via#ran","syntology_url":"https://syntology.ai/paper/2312.17090","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17090"}}}},{"paper":"/paper/topiq-a-top-down-approach-from-semantics-to","slug":"topiq-a-top-down-approach-from-semantics-to","title":"TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment","date":"2023-08-06","arxiv_id":"2308.03060","rows_on_this_dataset":8,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":6,"pointer_only_for_licence":10,"official":{"repos":["chaofengc/iqa-pytorch"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/topiq-a-top-down-approach-from-semantics-to#ran","syntology_url":"https://syntology.ai/paper/2308.03060","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03060"}}}},{"paper":"/paper/shift-tolerant-perceptual-similarity-metric-1","slug":"shift-tolerant-perceptual-similarity-metric-1","title":"Shift-tolerant Perceptual Similarity Metric","date":"2022-07-27","arxiv_id":"2207.13686","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":21,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":6,"samples_unverified":13,"pointer_only_for_licence":4,"official":{"repos":["abhijay9/shifttolerant-lpips"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":13,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/shift-tolerant-perceptual-similarity-metric-1#ran","syntology_url":"https://syntology.ai/paper/2207.13686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.13686"}}}},{"paper":"/paper/maniqa-multi-dimension-attention-network-for","slug":"maniqa-multi-dimension-attention-network-for","title":"MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment","date":"2022-04-19","arxiv_id":"2204.08958","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["iigroup/maniqa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/maniqa-multi-dimension-attention-network-for#ran","syntology_url":"https://syntology.ai/paper/2204.08958","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08958"}}}},{"paper":"/paper/musiq-multi-scale-image-quality-transformer","slug":"musiq-multi-scale-image-quality-transformer","title":"MUSIQ: Multi-scale Image Quality Transformer","date":"2021-08-12","arxiv_id":"2108.05997","rows_on_this_dataset":4,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":5,"samples_constructed":1,"samples_ran_checked":4,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":6,"official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/musiq-multi-scale-image-quality-transformer#ran","syntology_url":"https://syntology.ai/paper/2108.05997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05997"}}}},{"paper":"/paper/norm-in-norm-loss-with-faster-convergence-and","slug":"norm-in-norm-loss-with-faster-convergence-and","title":"Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment","date":"2020-08-10","arxiv_id":"2008.03889","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":3,"official":{"repos":["lidq92/LinearityIQA"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/norm-in-norm-loss-with-faster-convergence-and#ran","syntology_url":"https://syntology.ai/paper/2008.03889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.03889"}}}},{"paper":"/paper/image-quality-assessment-unifying-structure","slug":"image-quality-assessment-unifying-structure","title":"Image Quality Assessment: Unifying Structure and Texture Similarity","date":"2020-04-16","arxiv_id":"2004.07728","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["dingkeyan93/DISTS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/image-quality-assessment-unifying-structure#ran","syntology_url":"https://syntology.ai/paper/2004.07728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07728"}}}},{"paper":"/paper/quality-assessment-of-in-the-wild-videos","slug":"quality-assessment-of-in-the-wild-videos","title":"Quality Assessment of In-the-Wild Videos","date":"2019-08-01","arxiv_id":"1908.00375","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["lidq92/VSFA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/quality-assessment-of-in-the-wild-videos#ran","syntology_url":"https://syntology.ai/paper/1908.00375","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.00375"}}}},{"paper":"/paper/blind-image-quality-assessment-using-a-deep","slug":"blind-image-quality-assessment-using-a-deep","title":"Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network","date":"2019-07-05","arxiv_id":"1907.02665","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":3,"samples_unverified":1,"pointer_only_for_licence":3,"official":{"repos":["zwx8981/DBCNN-PyTorch"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/blind-image-quality-assessment-using-a-deep#ran","syntology_url":"https://syntology.ai/paper/1907.02665","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.02665"}}}},{"paper":"/paper/the-unreasonable-effectiveness-of-deep","slug":"the-unreasonable-effectiveness-of-deep","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","date":"2018-01-11","arxiv_id":"1801.03924","rows_on_this_dataset":2,"code_links":24,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["richzhang/PerceptualSimilarity"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/the-unreasonable-effectiveness-of-deep#ran","syntology_url":"https://syntology.ai/paper/1801.03924","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.03924"}}}}],"record_sha256":"83ce5714dcf7e0b6d8a81203ebfc356ea2ecd1e3feac1ddd422e3f940f3d0ddb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}