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

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation