Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation

• URL: http://arxiv.org/abs/2309.04573v2
• Date: Tue, 12 Sep 2023 14:36:02 GMT
• Title: Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation
• Authors: Shyam Nandan Rai, Fabio Cermelli, Barbara Caputo, Carlo Masone
• Abstract summary: 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 a mask-classification architecture. By comprehensive qualitative and qualitative evaluation, we show Mask2Anomaly achieves new state-of-the-art results.
• Score: 29.43462426812185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmenting unknown or anomalous object instances is a critical task in autonomous 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 a mask-classification architecture to jointly address anomaly segmentation, open-set semantic segmentation, and open-set panoptic segmentation. Mask2Anomaly includes several technical novelties that are designed to improve the detection of anomalies/unknown objects: 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; iii) a mask refinement solution to reduce false positives; and iv) a novel approach to mine unknown instances based on the mask-architecture properties. By comprehensive qualitative and qualitative evaluation, we show Mask2Anomaly achieves new state-of-the-art results across the benchmarks of anomaly segmentation, open-set semantic segmentation, and open-set panoptic segmentation.

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