  In this paper we present Mask DINO, a unified object detection and
segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising
Anchor Boxes) by adding a mask prediction branch which supports all image
segmentation tasks (instance, panoptic, and semantic). It makes use of the
query embeddings from DINO to dot-product a high-resolution pixel embedding map
to predict a set of binary masks. Some key components in DINO are extended for
segmentation through a shared architecture and training process. Mask DINO is
simple, efficient, and scalable, and it can benefit from joint large-scale
detection and segmentation datasets. Our experiments show that Mask DINO
significantly outperforms all existing specialized segmentation methods, both
on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably,
Mask DINO establishes the best results to date on instance segmentation (54.5
AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation
(60.8 mIoU on ADE20K) among models under one billion parameters. Code is
available at \url{https://github.com/IDEACVR/MaskDINO}.
