Papers › ExFuse: Enhancing Feature Fusion for Semantic Segmentation
ExFuse: Enhancing Feature Fusion for Semantic Segmentation
Zhenli Zhang, Xiangyu Zhang, Chao Peng, Dazhi Cheng, Jian Sun
Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we first point out that a simple fusion of low-level and high-level features could be less effective because of the gap in semantic levels and spatial resolution. We find that introducing semantic information into low-level features and high-resolution details into high-level features is more effective for the later fusion. Based on this observation, we propose a new framework, named ExFuse, to bridge the gap between low-level and high-level features thus significantly improve the segmentation quality by 4.0\% in total. Furthermore, we evaluate our approach on the challenging PASCAL VOC 2012 segmentation benchmark and achieve 87.9\% mean IoU, which outperforms the previous state-of-the-art results.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | PASCAL VOC 2012 val | ExFuse (ResNeXt-131) | mIoU | 85.8% | #4 of 29 | Archive leaderboard | report |
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