Segment Every Out-of-Distribution Object

• URL: http://arxiv.org/abs/2311.16516v4
• Date: Thu, 28 Mar 2024 15:15:04 GMT
• Title: Segment Every Out-of-Distribution Object
• Authors: Wenjie Zhao, Jia Li, Xin Dong, Yu Xiang, Yunhui Guo,
• Abstract summary: This paper introduces a method to convert anomaly textbfScore textbfTo segmentation textbfMask, called S2M, a simple and effective framework for OoD detection in semantic segmentation. By transforming anomaly scores into prompts for a promptable segmentation model, S2M eliminates the need for threshold selection.
• Score: 24.495734304922244
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic segmentation models, while effective for in-distribution categories, face challenges in real-world deployment due to encountering out-of-distribution (OoD) objects. Detecting these OoD objects is crucial for safety-critical applications. Existing methods rely on anomaly scores, but choosing a suitable threshold for generating masks presents difficulties and can lead to fragmentation and inaccuracy. This paper introduces a method to convert anomaly \textbf{S}core \textbf{T}o segmentation \textbf{M}ask, called S2M, a simple and effective framework for OoD detection in semantic segmentation. Unlike assigning anomaly scores to pixels, S2M directly segments the entire OoD object. By transforming anomaly scores into prompts for a promptable segmentation model, S2M eliminates the need for threshold selection. Extensive experiments demonstrate that S2M outperforms the state-of-the-art by approximately 20% in IoU and 40% in mean F1 score, on average, across various benchmarks including Fishyscapes, Segment-Me-If-You-Can, and RoadAnomaly datasets.

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