Papers › MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
Chenglin Yang, Siyuan Qiao, Qihang Yu, Xiaoding Yuan, Yukun Zhu, Alan Yuille, Hartwig Adam, Liang-Chieh Chen
This paper presents MOAT, a family of neural networks that build on top of MObile convolution (i.e., inverted residual blocks) and ATtention. Unlike the current works that stack separate mobile convolution and transformer blocks, we effectively merge them into a MOAT block. Starting with a standard Transformer block, we replace its multi-layer perceptron with a mobile convolution block, and further reorder it before the self-attention operation. The mobile convolution block not only enhances the network representation capacity, but also produces better downsampled features. Our conceptually simple MOAT networks are surprisingly effective, achieving 89.1% / 81.5% top-1 accuracy on ImageNet-1K / ImageNet-1K-V2 with ImageNet22K pretraining. Additionally, MOAT can be seamlessly applied to downstream tasks that require large resolution inputs by simply converting the global attention to window attention. Thanks to the mobile convolution that effectively exchanges local information between pixels (and thus cross-windows), MOAT does not need the extra window-shifting mechanism. As a result, on COCO object detection, MOAT achieves 59.2% box AP with 227M model parameters (single-scale inference, and hard NMS), and on ADE20K semantic segmentation, MOAT attains 57.6% mIoU with 496M model parameters (single-scale inference). Finally, the tiny-MOAT family, obtained by simply reducing the channel sizes, also surprisingly outperforms several mobile-specific transformer-based models on ImageNet. The tiny-MOAT family is also benchmarked on downstream tasks, serving as a baseline for the community. We hope our simple yet effective MOAT will inspire more seamless integration of convolution and self-attention. Code is publicly available.
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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 |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | MOAT-4 22K+1K | GFLOPs | 648.5 | #25 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MOAT-4 22K+1K | Number of params | 483.2M | #25 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MOAT-4 22K+1K | Top 1 Accuracy | 89.1% | #25 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MOAT-3 1K only | GFLOPs | 271 | #129 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MOAT-3 1K only | Number of params | 190M | #129 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MOAT-3 1K only | Top 1 Accuracy | 86.7% | #129 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MOAT-0 1K only | GFLOPs | 5.7 | #439 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MOAT-0 1K only | Number of params | 27.8M | #439 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MOAT-0 1K only | Top 1 Accuracy | 83.3% | #439 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet V2 | MOAT-4 (IN-22K pretraining) | Top 1 Accuracy | 81.5 | #8 of 33 | Archive leaderboard | report |
| Image Classification | ImageNet V2 | MOAT-3 (IN-22K pretraining) | Top 1 Accuracy | 80.6 | #10 of 33 | Archive leaderboard | report |
| Image Classification | ImageNet V2 | MOAT-2 (IN-22K pretraining) | Top 1 Accuracy | 79.3 | #11 of 33 | Archive leaderboard | report |
| Image Classification | ImageNet V2 | MOAT-1 (IN-22K pretraining) | Top 1 Accuracy | 78.4 | #12 of 33 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | MOAT-3 (IN-22K pretraining, single-scale) | mask AP | 50.3 | #25 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | MOAT-2 (IN-22K pretraining, single-scale) | mask AP | 49.3 | #28 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | MOAT-1 (IN-1K pretraining, single-scale) | mask AP | 49.0 | #29 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | MOAT-0 (IN-1K pretraining, single-scale) | mask AP | 47.4 | #38 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | tiny-MOAT-3 (IN-1K pretraining, single-scale) | mask AP | 47.0 | #41 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | tiny-MOAT-2 (IN-1K pretraining, single-scale) | mask AP | 45.0 | #49 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | tiny-MOAT-1 (IN-1K pretraining, single-scale) | mask AP | 44.6 | #51 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | tiny-MOAT-0 (IN-1K pretraining, single-scale) | mask AP | 43.3 | #61 of 93 | Archive leaderboard | report |
| Object Detection | COCO (Common Objects in Context) | MOAT-3 22K+1K | box AP | 59.2 | #1 of 3 | Archive leaderboard | report |
| Object Detection | COCO (Common Objects in Context) | MOAT-2 | box AP | 58.5 | #2 of 3 | Archive leaderboard | report |
| Object Detection | COCO minival | MOAT-3 (IN-22K pretraining, single-scale) | box AP | 59.2 | #31 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | MOAT-2 (IN-22K pretraining, single-scale) | box AP | 58.5 | #35 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | MOAT-1 (IN-1K pretraining, single-scale) | box AP | 57.7 | #38 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | MOAT-0 (IN-1K pretraining, single-scale) | box AP | 55.9 | #47 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | tiny-MOAT-3 (IN-1K pretraining, single-scale) | box AP | 55.2 | #50 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | tiny-MOAT-2 (IN-1K pretraining, single-scale) | box AP | 53.0 | #63 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | tiny-MOAT-1 (IN-1K pretraining, single-scale) | box AP | 51.9 | #69 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | tiny-MOAT-0 (IN-1K pretraining, single-scale) | box AP | 50.5 | #78 of 220 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | MOAT-4 (IN-22K pretraining, single-scale) | Params (M) | 496 | #30 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | MOAT-4 (IN-22K pretraining, single-scale) | Validation mIoU | 57.6 | #30 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | MOAT-3 (IN-22K pretraining, single-scale) | Params (M) | 198 | #39 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | MOAT-3 (IN-22K pretraining, single-scale) | Validation mIoU | 56.5 | #39 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | MOAT-2 (IN-22K pretraining, single-scale) | Params (M) | 81 | #56 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | MOAT-2 (IN-22K pretraining, single-scale) | Validation mIoU | 54.7 | #56 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | tiny-MOAT-3 (IN-1K pretraining, single scale) | Params (M) | 24 | #163 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | tiny-MOAT-3 (IN-1K pretraining, single scale) | Validation mIoU | 47.5 | #163 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | tiny-MOAT-2 (IN-1K pretraining, single scale) | Params (M) | 13 | #198 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | tiny-MOAT-2 (IN-1K pretraining, single scale) | Validation mIoU | 44.9 | #198 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | tiny-MOAT-1 (IN-1K pretraining, single scale) | Params (M) | 8 | #212 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | tiny-MOAT-1 (IN-1K pretraining, single scale) | Validation mIoU | 43.1 | #212 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | tiny-MOAT-0 (IN-1K pretraining, single scale) | Params (M) | 6 | #216 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | tiny-MOAT-0 (IN-1K pretraining, single scale) | Validation mIoU | 41.2 | #216 of 235 | 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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