Scene-Centric Unsupervised Panoptic Segmentation
This work addresses the problem of scene understanding for computer vision applications by enabling panoptic segmentation without object-centric training data, representing a significant advance over incremental improvements.
The paper tackles unsupervised panoptic segmentation for complex scenes without human annotations, achieving a 9.4% improvement in panoptic quality on Cityscapes compared to prior state-of-the-art methods.
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In contrast to prior work on unsupervised panoptic scene understanding, we eliminate the need for object-centric training data, enabling the unsupervised understanding of complex scenes. To that end, we present the first unsupervised panoptic method that directly trains on scene-centric imagery. In particular, we propose an approach to obtain high-resolution panoptic pseudo labels on complex scene-centric data, combining visual representations, depth, and motion cues. Utilizing both pseudo-label training and a panoptic self-training strategy yields a novel approach that accurately predicts panoptic segmentation of complex scenes without requiring any human annotations. Our approach significantly improves panoptic quality, e.g., surpassing the recent state of the art in unsupervised panoptic segmentation on Cityscapes by 9.4% points in PQ.