{"url":"/task/anomaly-severity-classification-anomaly-vs","name":"Anomaly Severity Classification (Anomaly vs. Defect)","slug":"anomaly-severity-classification-anomaly-vs","description_markdown":"Anomaly Severity Classification is a novel task that extends traditional anomaly detection by distinguishing between negligible anomalies and critical defects. While existing benchmarks focus on identifying whether an input deviates from normality, this task introduces a deployment-oriented perspective—evaluating the actionability of detected anomalies. For example, cosmetic deviations or design shifts may be acceptable (anomalies), while structural damages require intervention (defects). This task supports more informed and automated decision-making in industrial and quality control settings. It was introduced in the VELM paper, alongside a simulation benchmark derived from the MVTec-AC dataset.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":1,"papers_with_code":1,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":1,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/anomaly-severity-classification-anomaly-vs","slug":"anomaly-severity-classification-anomaly-vs","dataset":"MVTec-AC","dataset_url":"/dataset/mvtec-ac","rows_in_archive":1,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"VELM","paper_title":"Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models","paper_url":"/paper/detect-classify-act-categorizing-industrial","paper_date":"2025-05-05","arxiv_id":"2505.02626","code_links":[{"title":"sassanmtr/velm","url":"https://github.com/sassanmtr/velm"}],"syntology":null}}],"datasets":[{"url":"/dataset/mvtec-ac","name":"MVTec-AC","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/anomaly-classification","name":"Anomaly Classification"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":1,"of":1,"tagged_in_all":1,"items":[{"url":"/paper/detect-classify-act-categorizing-industrial","title":"Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models","date":"2025-05-05","arxiv_id":"2505.02626","repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}