Unmasking Anomalies in Road-Scene Segmentation
Shyam Nandan Rai Fabio Cermelli Dario Fontanel Carlo Masone Barbara Caputo
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.