Awesome

🚀🚀🚀 🔥🔥🔥 H-Mask-Deformable-DETR

News

2022.08.31 We will also release the code for H-Mask-Deformable-DETR soon (strong results on both instance segmentation and panoptic segmentation).

Introduction

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) method to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to a wide range of visual problems, including instance/semantic segmentation, human pose estimation, and point cloud/multi-view-images based detection, etc. However, we note that because there are too few queries assigned as positive samples, the one-to-one set matching significantly reduces the training efficiency of positive samples. This paper proposes a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with auxiliary queries that use one-to-many matching loss during training. This hybrid strategy has been shown to significantly improve training efficiency and improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, 3DETR/PETRv2, PETR, and TransTrack, among others.

Citing H-DETR

If you find H-DETR useful in your research, please consider citing:

@article{jia2022detrs,
  title={DETRs with Hybrid Matching},
  author={Jia, Ding and Yuan, Yuhui and He, Haodi and Wu, Xiaopei and Yu, Haojun and Lin, Weihong and Sun, Lei and Zhang, Chao and Hu, Han},
  journal={arXiv preprint arXiv:2207.13080},
  year={2022}
}