Papers › Dense Distinct Query for End-to-End Object Detection
Dense Distinct Query for End-to-End Object Detection
Shilong Zhang, Xinjiang Wang, Jiaqi Wang, Jiangmiao Pang, Chengqi Lyu, Wenwei Zhang, Ping Luo, Kai Chen
One-to-one label assignment in object detection has successfully obviated the need for non-maximum suppression (NMS) as 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 encounter optimization difficulties. 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. The source code can be found at \url{https://github.com/jshilong/DDQ}.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | CrowdHuman (full body) | DDQ DETR (R50) | AP | 93.8 | #4 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | DDQ DETR (R50) | Recall | 98.7 | #4 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | DDQ DETR (R50) | mMR | 39.7 | #4 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | DDQ R-CNN (R50) | AP | 93.5 | #5 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | DDQ R-CNN (R50) | Recall | 98.6 | #5 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | DDQ R-CNN (R50) | mMR | 40.4 | #5 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | DDQ FCN (R50 One-Stage) | AP | 92.7 | #7 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | DDQ FCN (R50 One-Stage) | Recall | 98.2 | #7 of 19 | Archive leaderboard | report |
| Object Detection | CrowdHuman (full body) | DDQ FCN (R50 One-Stage) | mMR | 41.0 | #7 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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