Panoptic SegFormer

• URL: http://arxiv.org/abs/2109.03814v1
• Date: Wed, 8 Sep 2021 17:59:12 GMT
• Title: Panoptic SegFormer
• Authors: Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Tong Lu, Ping Luo
• Abstract summary: We present Panoptic-SegFormer, a framework for end-to-end panoptic segmentation with Transformers. With a ResNet-50 backbone, our method achieves 50.0% PQ on the COCO test-dev split. Using a more powerful PVTv2-B5 backbone, Panoptic-SegFormer achieves a new record of 54.1%PQ and 54.4% PQ on the COCO val and test-dev splits with single scale input.
• Score: 82.6258003344804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers. The proposed method extends Deformable DETR with a unified mask prediction workflow for both things and stuff, making the panoptic segmentation pipeline concise and effective. With a ResNet-50 backbone, our method achieves 50.0\% PQ on the COCO test-dev split, surpassing previous state-of-the-art methods by significant margins without bells and whistles. Using a more powerful PVTv2-B5 backbone, Panoptic-SegFormer achieves a new record of 54.1\%PQ and 54.4\% PQ on the COCO val and test-dev splits with single scale input.

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