Locate and segment atypical objects better by being instance-aware
Locate and segment atypical objects better by being instance-aware
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation
arXiv paper abstract https://arxiv.org/abs/2207.08782v1
arXiv PDF paper https://arxiv.org/pdf/2207.08782v1.pdf
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.
To address this ... provide instance knowledge to the observer ... extend the approach of ObsNet by harnessing an instance-wise mask prediction.
... use an additional, class agnostic, object detector to filter and aggregate observer predictions.
... predict an unique anomaly score for each instance in the image.
... show that ... proposed method accurately disentangle in-distribution objects from Out-Of-Distribution objects on three datasets.
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