Scene-Centric Unsupervised Panoptic Segmentation
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.
67f00313579927adf0ee1d8b
Oliver Hahn
claimed_verified
2025-04-08T06:53:34.223Z
636020de88e41d249eccf68c
https://cdn-avatars.huggingface.co/v1/production/uploads/636020de88e41d249eccf68c/pMduOM5UNZo4JW2Ckk2jK.jpeg
Oliver Hahn
user
olvrhhn
67f00313579927adf0ee1d8c
Christoph Reich
claimed_verified
2025-04-06T08:11:31.401Z
67f001a5f824e54a66563455
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/AG6K_dOseLcc84tHP7V8D.png
Christoph Reich
user
ChristophReich1996
67f00313579927adf0ee1d8d
Nikita Araslanov
67f00313579927adf0ee1d8e
Daniel Cremers
67f00313579927adf0ee1d8f
Christian Rupprecht
67f00313579927adf0ee1d90
Stefan Roth
Scene-Centric Unsupervised Panoptic Segmentation
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.
A novel unsupervised panoptic segmentation method combines visual representations, depth, and motion cues to achieve state-of-the-art performance on Cityscapes without human annotations.
unsupervised panoptic segmentation panoptic pseudo labels scene-centric imagery panoptic self-training strategy Cityscapes PQ