Papers › DiCENet: Dimension-wise Convolutions for Efficient Networks
DiCENet: Dimension-wise Convolutions for Efficient Networks
Sachin Mehta, Hannaneh Hajishirzi, Mohammad Rastegari
We introduce a novel and generic convolutional unit, DiCE unit, that is built using dimension-wise convolutions and dimension-wise fusion. The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the DiCE unit to efficiently encode spatial and channel-wise information contained in the input tensor. The DiCE unit is simple and can be seamlessly integrated with any architecture to improve its efficiency and performance. Compared to depth-wise separable convolutions, the DiCE unit shows significant improvements across different architectures. When DiCE units are stacked to build the DiCENet model, we observe significant improvements over state-of-the-art models across various computer vision tasks including image classification, object detection, and semantic segmentation. On the ImageNet dataset, the DiCENet delivers 2-4% higher accuracy than state-of-the-art manually designed models (e.g., MobileNetv2 and ShuffleNetv2). Also, DiCENet generalizes better to tasks (e.g., object detection) that are often used in resource-constrained devices in comparison to state-of-the-art separable convolution-based efficient networks, including neural search-based methods (e.g., MobileNetv3 and MixNet. Our source code in PyTorch is open-source and is available at https://github.com/sacmehta/EdgeNets/
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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 | DiCENet | GFLOPs | 0.553 | #957 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | DiCENet | Top 1 Accuracy | 75.1% | #957 of 1060 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | DiCENet | mIoU | 63.4 | #93 of 99 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | DiCENet | Mean IoU | 67.31% | #43 of 51 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 val | DiCENet | mIoU | 66.5% | #21 of 29 | 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: DiCE Unit, DiCENet
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