Papers › Pyramid Feature Attention Network for Saliency detection
Pyramid Feature Attention Network for Saliency detection
Ting Zhao, Xiangqian Wu
Saliency detection is one of the basic challenges in computer vision. How to extract effective features is a critical point for saliency detection. Recent methods mainly adopt integrating multi-scale convolutional features indiscriminately. However, not all features are useful for saliency detection and some even cause interferences. To solve this problem, we propose Pyramid Feature Attention network to focus on effective high-level context features and low-level spatial structural features. First, we design Context-aware Pyramid Feature Extraction (CPFE) module for multi-scale high-level feature maps to capture rich context features. Second, we adopt channel-wise attention (CA) after CPFE feature maps and spatial attention (SA) after low-level feature maps, then fuse outputs of CA & SA together. Finally, we propose an edge preservation loss to guide network to learn more detailed information in boundary localization. Extensive evaluations on five benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches under different evaluation metrics.
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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 |
|---|---|---|---|---|---|---|---|
| Saliency Detection | DUT-OMRON | Pyramid Feature Attention | MAE | 0.0414 | #1 of 5 | Archive leaderboard | report |
| Saliency Detection | DUTS-test | Pyramid Feature Attention | MAE | 0.0405 | #2 of 2 | Archive leaderboard | report |
| Saliency Detection | ECSSD | Pyramid Feature Attention | MAE | 0.0328 | #1 of 1 | Archive leaderboard | report |
| Saliency Detection | HKU-IS | Pyramid Feature Attention | MAE | 0.0324 | #2 of 3 | Archive leaderboard | report |
| Saliency Detection | PASCAL-S | Pyramid Feature Attention | MAE | 0.0677 | #1 of 1 | 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.
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