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Panoptic segmentation involves a combination of joint semantic segmentationand instance segmentation, where image contents are divided into two types:things and stuff. We present Panoptic SegFormer, a general framework forpanoptic segmentation with transformers. It contains three innovativecomponents: an efficient deeply-supervised mask decoder, a query decouplingstrategy, and an improved post-processing method. We also use Deformable DETRto efficiently process multi-scale features, which is a fast and efficientversion of DETR. Specifically, we supervise the attention modules in the maskdecoder in a layer-wise manner. This deep supervision strategy lets theattention modules quickly focus on meaningful semantic regions. It improvesperformance and reduces the number of required training epochs by half comparedto Deformable DETR. Our query decoupling strategy decouples theresponsibilities of the query set and avoids mutual interference between thingsand stuff. In addition, our post-processing strategy improves performancewithout additional costs by jointly considering classification and segmentationqualities to resolve conflicting mask overlaps. Our approach increases theaccuracy 6.2\% PQ over the baseline DETR model. Panoptic SegFormer achievesstate-of-the-art results on COCO test-dev with 56.2\% PQ. It also showsstronger zero-shot robustness over existing methods. The code is released at\url{https://github.com/zhiqi-li/Panoptic-SegFormer}.

Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers