  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.
