Dense Distinct Query for End-to-End Object Detection

Overall framework

Abstract

One-to-one label assignment in object detection has successfully obviated the need of non-maximum suppression (NMS) as a postprocessing and makes the pipeline end-to-end. However, it triggers a new dilemma as the widely used sparse queries cannot guarantee a high recall, while dense queries inevitably bring more similar queries and encounters optimization difficulty. As both sparse and dense queries are problematic, then what are the expected queries in end-to-end object detection? This paper shows that the solution should be Dense Distinct Queries (DDQ). Concretely, we first lay dense queries like traditional detectors and then select distinct ones for one-to-one assignments. DDQ blends the advantages of traditional and recent end-to-end detectors and significantly improves the performance of various detectors including FCN, R-CNN, and DETRs. Most impressively, DDQ-DETR achieves 52.1 AP on MS-COCO dataset within 12 epochs using a ResNet-50 backbone, outperforming all existing detectors in the same setting. DDQ also shares the benefit of end-to-end detectors in crowded scenes and achieves 93.8 AP on CrowdHuman. We hope DDQ can inspire researchers to consider the complementarity between traditional methods and end-to-end detectors.

Publication
In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023
Jiaqi Wang 王佳琦
Jiaqi Wang 王佳琦
Research Scientist & Team Leader
Shanghai AI Laboratory

Jiaqi Wang is currently a Research Scientist and Team Leader at Shanghai AI Laboratory, as well as an Adjunct Ph.D. Supervisor at Shanghai Jiao Tong University. His research focuses on visual perception, vision-language models, and the development of benchmarks and datasets in these areas.