Papers › MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism
MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism
Zhixiong Nan, Xianghong Li, Jifeng Dai, Tao Xiang
Based on analyzing the character of cascaded decoder architecture commonly adopted in existing DETR-like models, this paper proposes a new decoder architecture. The cascaded decoder architecture constrains object queries to update in the cascaded direction, only enabling object queries to learn relatively-limited information from image features. However, the challenges for object detection in natural scenes (e.g., extremely-small, heavily-occluded, and confusingly mixed with the background) require an object detection model to fully utilize image features, which motivates us to propose a new decoder architecture with the parallel Multi-time Inquiries (MI) mechanism. MI enables object queries to learn more comprehensive information, and our MI based model, MI-DETR, outperforms all existing DETR-like models on COCO benchmark under different backbones and training epochs, achieving +2.3 AP and +0.6 AP improvements compared to the most representative model DINO and SOTA model Relation-DETR under ResNet-50 backbone. In addition, a series of diagnostic and visualization experiments demonstrate the effectiveness, rationality, and interpretability of MI.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | COCO 2017 val | MI-DETR (Swin-L 1x) | AP | 58.2 | #3 of 33 | Archive leaderboard | report |
| Object Detection | COCO 2017 val | MI-DETR (Swin-L 1x) | AP50 | 76.5 | #3 of 33 | Archive leaderboard | report |
| Object Detection | COCO 2017 val | MI-DETR (Swin-L 1x) | AP75 | 63.4 | #3 of 33 | Archive leaderboard | report |
| Object Detection | COCO 2017 val | MI-DETR (Swin-L 1x) | APL | 74.6 | #3 of 33 | Archive leaderboard | report |
| Object Detection | COCO 2017 val | MI-DETR (Swin-L 1x) | APM | 62.8 | #3 of 33 | Archive leaderboard | report |
| Object Detection | COCO 2017 val | MI-DETR (Swin-L 1x) | APS | 42.5 | #3 of 33 | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections