Papers › Scale-Aware Trident Networks for Object Detection
Scale-Aware Trident Networks for Object Detection
Yanghao Li, Yuntao Chen, Naiyan Wang, Zhao-Xiang Zhang
Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection. Based on the findings from the exploration experiments, we propose a novel Trident Network (TridentNet) aiming to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. As a bonus, a fast approximation version of TridentNet could achieve significant improvements without any additional parameters and computational cost compared with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP. Codes are available at https://git.io/fj5vR.
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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 | COCO minival | TridentNet (ResNet-101) | AP50 | 63.5 | #158 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | TridentNet (ResNet-101) | AP75 | 45.5 | #158 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | TridentNet (ResNet-101) | APL | 56.9 | #158 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | TridentNet (ResNet-101) | APM | 47 | #158 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | TridentNet (ResNet-101) | APS | 24.9 | #158 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | TridentNet (ResNet-101) | box AP | 42 | #158 of 220 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101-Deformable, Image Pyramid) | AP50 | 69.7 | #105 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101-Deformable, Image Pyramid) | AP75 | 53.5 | #105 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101-Deformable, Image Pyramid) | APL | 60.3 | #105 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101-Deformable, Image Pyramid) | APM | 51.3 | #105 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101-Deformable, Image Pyramid) | APS | 31.8 | #105 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101-Deformable, Image Pyramid) | box mAP | 48.4 | #105 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101) | AP50 | 63.6 | #170 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101) | AP75 | 46.5 | #170 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101) | APL | 56.6 | #170 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101) | APM | 46.6 | #170 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101) | APS | 23.9 | #170 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | TridentNet (ResNet-101) | box mAP | 42.7 | #170 of 225 | 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
Introduced by this paper: TridentNet, TridentNet Block
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