Papers › Learning Uncertain Convolutional Features for Accurate Saliency Detection
Learning Uncertain Convolutional Features for Accurate Saliency Detection
Pingping Zhang, Dong Wang, Huchuan Lu, Hongyu Wang, Bao-Cai Yin
Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which encourage the robustness and accuracy of saliency detection. We achieve this via introducing a reformulated dropout (R-dropout) after specific convolutional layers to construct an uncertain ensemble of internal feature units. In addition, we propose an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in our decoder network. The proposed methods can also be applied to other deep convolutional networks. Compared with existing saliency detection methods, the proposed UCF model is able to incorporate uncertainties for more accurate object boundary inference. Extensive experiments demonstrate that our proposed saliency model performs favorably against state-of-the-art approaches. The uncertain feature learning mechanism as well as the upsampling method can significantly improve performance on other pixel-wise vision tasks.
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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 |
|---|---|---|---|---|---|---|---|
| RGB Salient Object Detection | DUTS-TE | UCF | MAE | 0.116 | #28 of 31 | Archive leaderboard | report |
| RGB Salient Object Detection | DUTS-TE | UCF | max F-measure | 0.771 | #28 of 31 | Archive leaderboard | report |
| Saliency Detection | DUT-OMRON | UCF | MAE | 0.1203 | #5 of 5 | 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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