{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/unmasking-anomalies-in-road-scene","title":"Unmasking Anomalies in Road-Scene Segmentation","arxiv_id":"2307.13316","date":"2023-07-25","proceeding":"ICCV 2023 1","authors":["Shyam Nandan Rai","Fabio Cermelli","Dario Fontanel","Carlo Masone","Barbara Caputo"],"abstract":"Anomaly segmentation is a critical task for driving applications, and it is approached traditionally as a per-pixel classification problem. However, reasoning individually about each pixel without considering their contextual semantics results in high uncertainty around the objects' boundaries and numerous false positives. We propose a paradigm change by shifting from a per-pixel classification to a mask classification. Our mask-based method, Mask2Anomaly, demonstrates the feasibility of integrating an anomaly detection method in a mask-classification architecture. Mask2Anomaly includes several technical novelties that are designed to improve the detection of anomalies in masks: i) a global masked attention module to focus individually on the foreground and background regions; ii) a mask contrastive learning that maximizes the margin between an anomaly and known classes; and iii) a mask refinement solution to reduce false positives. Mask2Anomaly achieves new state-of-the-art results across a range of benchmarks, both in the per-pixel and component-level evaluations. In particular, Mask2Anomaly reduces the average false positives rate by 60% wrt the previous state-of-the-art. Github page: https://github.com/shyam671/Mask2Anomaly-Unmasking-Anomalies-in-Road-Scene-Segmentation.","url_abs":"https://arxiv.org/abs/2307.13316v1","url_pdf":"https://arxiv.org/pdf/2307.13316v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unmasking-anomalies-in-road-scene","repo_url":"https://github.com/shyam671/mask2anomaly-unmasking-anomalies-in-road-scene-segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"anomaly-segmentation","task_name":"Anomaly Segmentation"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"scene-segmentation","task_name":"Scene Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-1","task":"Anomaly Detection","dataset":"Fishyscapes","model":"Mask2Anomaly","rank_in_archive_order":2,"of":8,"metrics":{"AP":"95.20","FPR95":"0.82"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-l-f","task":"Anomaly Detection","dataset":"Fishyscapes L&F","model":"Mask2Anomaly","rank_in_archive_order":6,"of":18,"metrics":{"AP":"46.04","FPR95":"4.36"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-lost-and-found","task":"Anomaly Detection","dataset":"Lost and Found","model":"Mask2Anomaly","rank_in_archive_order":1,"of":4,"metrics":{"AP":"86.59","FPR":"5.75"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-road-anomaly","task":"Anomaly Detection","dataset":"Road Anomaly","model":"Mask2Anomaly","rank_in_archive_order":5,"of":10,"metrics":{"AP":"79.70","FPR95":"13.45"},"uses_additional_data":true},{"leaderboard":"/sota/instance-segmentation-on-oodis","task":"Instance Segmentation","dataset":"OoDIS","model":"Mask2Anomaly","rank_in_archive_order":2,"of":3,"metrics":{"AP":"13.73","AP50":"24.30"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-oodis","task":"Object Detection","dataset":"OoDIS","model":"Mask2Anomaly","rank_in_archive_order":2,"of":3,"metrics":{"AP":"1.24","AP50":"2.23"},"uses_additional_data":false},{"leaderboard":"/sota/scene-segmentation-on-streethazards","task":"Scene Segmentation","dataset":"StreetHazards","model":"Mask2Anomaly","rank_in_archive_order":1,"of":3,"metrics":{"Open-mIoU":"59.8"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2307.13316","atlas_url":"https://app.syntology.ai/?focus=2307.13316","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}