Instance-Aware Observer Network for Out-of-Distribution Object Segmentation

Recent work on Observer Network has shown promising results on Out-Of-Distribution (OOD) detection for semantic segmentation. These methods have difficulty in precisely locating the point of interest in the image, i.e, the anomaly. This limitation is due to the difficulty of fine-grained prediction at the pixel level. To address this issue, we provide instance knowledge to the observer. We extend the approach of ObsNet by harnessing an instance-wise mask prediction. We use an additional, class agnostic, object detector to filter and aggregate observer predictions. Finally, we predict an unique anomaly score for each instance in the image. We show that our proposed method accurately disentangle in-distribution objects from Out-Of-Distribution objects on three datasets.