Unmasking Anomalies in Road-Scene Segmentation

• URL: http://arxiv.org/abs/2307.13316v1
• Date: Tue, 25 Jul 2023 08:23:10 GMT
• Title: Unmasking Anomalies in Road-Scene Segmentation
• Authors: Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel, Carlo Masone, Barbara Caputo
• Abstract summary: Anomaly segmentation is a critical task for driving applications. We propose a paradigm change by shifting from a per-pixel classification to a mask classification. Mask2Anomaly demonstrates the feasibility of integrating an anomaly detection method in a mask-classification architecture.
• Score: 18.253109627901566
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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