%0 Generic %1 li2022towards %A Li, Feng %A Zhang, Hao %A xu, Huaizhe %A Liu, Shilong %A Zhang, Lei %A Ni, Lionel M. %A Shum, Heung-Yeung %D 2022 %K segmentation %T Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation %U http://arxiv.org/abs/2206.02777 %X 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, scalable, and benefits 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). Code will be avaliable at https://github.com/IDEACVR/MaskDINO.

@misc{li2022towards, abstract = {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, scalable, and benefits 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). Code will be avaliable at \url{https://github.com/IDEACVR/MaskDINO}.}, added-at = {2022-07-17T16:37:43.000+0200}, author = {Li, Feng and Zhang, Hao and xu, Huaizhe and Liu, Shilong and Zhang, Lei and Ni, Lionel M. and Shum, Heung-Yeung}, biburl = {https://www.bibsonomy.org/bibtex/2a74b3f6bbf5357de4d599ec8c42acc55/redtedtezza}, description = {Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation}, interhash = {482cd835253a7a5212c1eb834dad2249}, intrahash = {a74b3f6bbf5357de4d599ec8c42acc55}, keywords = {segmentation}, note = {cite arxiv:2206.02777}, timestamp = {2022-07-17T16:37:43.000+0200}, title = {Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation}, url = {http://arxiv.org/abs/2206.02777}, year = 2022 }