Papers › Deformable Convolutional Networks
Deformable Convolutional Networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released.
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Code
Syntology Ran 3 of 9 code samples harvested from 4 repositories linked to this paper; 6 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · fixture could not drive it.
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Code Syntology ran Syntology
9 samples harvested; 3 ran; 1 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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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 test-dev | DeformConv-R-FCN (Aligned-Inception-ResNet) | AP50 | 58.0 | #218 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | DeformConv-R-FCN (Aligned-Inception-ResNet) | APL | 52.5 | #218 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | DeformConv-R-FCN (Aligned-Inception-ResNet) | APM | 40.1 | #218 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | DeformConv-R-FCN (Aligned-Inception-ResNet) | APS | 19.4 | #218 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | DeformConv-R-FCN (Aligned-Inception-ResNet) | box mAP | 37.5 | #218 of 225 | Archive leaderboard | report |
| Vessel Detection | Vessel detection Dateset | Deformable DETR | AP | 54.8% | #3 of 3 | 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: Deformable ConvNets, Deformable Convolution, Deformable Position-Sensitive RoI Pooling, Deformable RoI Pooling
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