Unsupervised Panoptic Segmentation

The contributions of this paper are two-fold. We define unsupervised techniques for the panoptic segmentation of an image. We also define clusters which encapsulate the set of features that define objects of interest inside a scene. The motivation is to provide an approach that mimics natural formation of ideas inside the brain. Fundamentally, the eyes and visual cortex constitute the visual system, which is essential for humans to detect and recognize objects. This can be done even without specific knowledge of the objects. We strongly believe that a supervisory signal should not be required to identify objects in an image. We present an algorithm that replaces the eye and visual cortex with deep learning architectures and unsupervised clustering methods. The proposed methodology may also be used as a one-click panoptic segmentation approach which promises to significantly increase annotation efficiency. We have made the code available privately for review¹.