Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation
This addresses safety-critical applications like automated driving by detecting unknown objects, but it is incremental as it builds on existing foreground-background segmentation approaches.
The paper tackles the problem of out-of-distribution (OOD) object segmentation in open-world scenarios by proposing a multi-scale method that uses foreground-background confidence scores, showing improved performance on the SegmentMeIfYouCan benchmark.
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.