Papers › X-volution: On the unification of convolution and self-attention
X-volution: On the unification of convolution and self-attention
Xuanhong Chen, Hang Wang, Bingbing Ni
Convolution and self-attention are acting as two fundamental building blocks in deep neural networks, where the former extracts local image features in a linear way while the latter non-locally encodes high-order contextual relationships. Though essentially complementary to each other, i.e., first-/high-order, stat-of-the-art architectures, i.e., CNNs or transformers lack a principled way to simultaneously apply both operations in a single computational module, due to their heterogeneous computing pattern and excessive burden of global dot-product for visual tasks. In this work, we theoretically derive a global self-attention approximation scheme, which approximates a self-attention via the convolution operation on transformed features. Based on the approximated scheme, we establish a multi-branch elementary module composed of both convolution and self-attention operation, capable of unifying both local and non-local feature interaction. Importantly, once trained, this multi-branch module could be conditionally converted into a single standard convolution operation via structural re-parameterization, rendering a pure convolution styled operator named X-volution, ready to be plugged into any modern networks as an atomic operation. Extensive experiments demonstrate that the proposed X-volution, achieves highly competitive visual understanding improvements (+1.2% top-1 accuracy on ImageNet classification, +1.7 box AP and +1.5 mask AP on COCO detection and segmentation).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | ResNet-50 (X-volution, stage3) | Top 1 Accuracy | 76.6% | #908 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ResNet-34 (X-volution, stage3) | Top 1 Accuracy | 75% | #960 of 1060 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | Mask R-CNN (FPN, X-volution, SA) | APL | 53.1 | #85 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | Mask R-CNN (FPN, X-volution, SA) | APM | 40 | #85 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | Mask R-CNN (FPN, X-volution, SA) | APS | 19.2 | #85 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | Mask R-CNN (FPN, X-volution, SA) | mask AP | 37.2 | #85 of 93 | Archive leaderboard | report |
| Object Detection | COCO minival | Faster R-CNN (FPN, X-volution) | AP50 | 64 | #149 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Faster R-CNN (FPN, X-volution) | AP75 | 46.4 | #149 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Faster R-CNN (FPN, X-volution) | APL | 55 | #149 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Faster R-CNN (FPN, X-volution) | APM | 46 | #149 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Faster R-CNN (FPN, X-volution) | APS | 26.9 | #149 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Faster R-CNN (FPN, X-volution) | box AP | 42.8 | #149 of 220 | 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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