Papers › Context Encoding for Semantic Segmentation
Context Encoding for Semantic Segmentation
Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal
Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-the-art results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpass the winning entry of COCO-Place Challenge in 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10 times more layers. The source code for the complete system are publicly available.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | ADE20K | EncNet | Test Score | 55.67 | #200 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | EncNet | Validation mIoU | 44.65 | #200 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | EncNet (ResNet-101) | mIoU | 44.65 | #85 of 95 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL Context | EncNet (ResNet-101) | mIoU | 51.7 | #50 of 66 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | EncNet | Mean IoU | 85.9% | #7 of 51 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | EncNet (ResNet-101) | Mean IoU | 82.9% | #21 of 51 | 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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