Papers › Context Prior for Scene Segmentation
Context Prior for Scene Segmentation
Changqian Yu, Jingbo Wang, Changxin Gao, Gang Yu, Chunhua Shen, Nong Sang
Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. However, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. In this work, we directly supervise the feature aggregation to distinguish the intra-class and inter-class context clearly. Specifically, we develop a Context Prior with the supervision of the Affinity Loss. Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior. The learned Context Prior extracts the pixels belonging to the same category, while the reversed prior focuses on the pixels of different classes. Embedded into a conventional deep CNN, the proposed Context Prior Layer can selectively capture the intra-class and inter-class contextual dependencies, leading to robust feature representation. To validate the effectiveness, we design an effective Context Prior Network (CPNet). Extensive quantitative and qualitative evaluations demonstrate that the proposed model performs favorably against state-of-the-art semantic segmentation approaches. More specifically, our algorithm achieves 46.3% mIoU on ADE20K, 53.9% mIoU on PASCAL-Context, and 81.3% mIoU on Cityscapes. Code is available at https://git.io/ContextPrior.
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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 |
|---|---|---|---|---|---|---|---|
| Scene Understanding | ADE20K val | CPN(ResNet-101) | Mean IoU | 46.3 | #1 of 1 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | CPN(ResNet-101) | Validation mIoU | 46.27 | #179 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | CPN(ResNet-101) | mIoU | 46.27 | #72 of 95 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | CPN(ResNet-101) | Mean IoU (class) | 81.3% | #44 of 105 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL Context | CPN(ResNet-101) | mIoU | 53.9 | #38 of 66 | 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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