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We present MaX-DeepLab, the first end-to-end model for panoptic segmentation.Our approach simplifies the current pipeline that depends heavily on surrogatesub-tasks and hand-designed components, such as box detection, non-maximumsuppression, thing-stuff merging, etc. Although these sub-tasks are tackled byarea experts, they fail to comprehensively solve the target task. By contrast,our MaX-DeepLab directly predicts class-labeled masks with a mask transformer,and is trained with a panoptic quality inspired loss via bipartite matching.Our mask transformer employs a dual-path architecture that introduces a globalmemory path in addition to a CNN path, allowing direct communication with anyCNN layers. As a result, MaX-DeepLab shows a significant 7.1% PQ gain in thebox-free regime on the challenging COCO dataset, closing the gap betweenbox-based and box-free methods for the first time. A small variant ofMaX-DeepLab improves 3.0% PQ over DETR with similar parameters and M-Adds.Furthermore, MaX-DeepLab, without test time augmentation, achieves newstate-of-the-art 51.3% PQ on COCO test-dev set. Code is available athttps://github.com/google-research/deeplab2.

MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers