Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation

• URL: http://arxiv.org/abs/2412.16990v1
• Date: Sun, 22 Dec 2024 12:09:27 GMT
• Title: Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation
• Authors: Samuel Marschall, Kira Maag,
• Abstract summary: We present a multi-scale OOD segmentation method that exploits the confidence information of a foreground-background segmentation model. We consider the per pixel confidence score of the model prediction which is close to 1 for a pixel in a foreground object. By aggregating these confidence values for different sized patches, objects of various sizes can be identified in a single image.
• Score: 0.36832029288386137
• License:
• Abstract: Deep neural networks have shown outstanding performance in computer vision tasks such as semantic segmentation and have defined the state-of-the-art. However, these segmentation models are trained on a closed and predefined set of semantic classes, which leads to significant prediction failures in open-world scenarios on unknown objects. As this behavior prevents the application in safety-critical applications such as automated driving, the detection and segmentation of these objects from outside their predefined semantic space (out-of-distribution (OOD) objects) is of the utmost importance. In this work, we present a multi-scale OOD segmentation method that exploits the confidence information of a foreground-background segmentation model. While semantic segmentation models are trained on specific classes, this restriction does not apply to foreground-background methods making them suitable for OOD segmentation. We consider the per pixel confidence score of the model prediction which is close to 1 for a pixel in a foreground object. By aggregating these confidence values for different sized patches, objects of various sizes can be identified in a single image. Our experiments show improved performance of our method in OOD segmentation compared to comparable baselines in the SegmentMeIfYouCan benchmark.

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