63 citations · 449 across the 39 of their papers we have counts for
6 papers · 1 filter
Uncertainty-Aware Deep Calibrated Salient Object Detection
Jing Zhang, Yuchao Dai, Xin Yu +3
Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. However, those methods overlook the gap between network ac…
3D Guided Weakly Supervised Semantic Segmentation
Weixuan Sun, Jing Zhang, Nick Barnes
Pixel-wise clean annotation is necessary for fully-supervised semantic segmentation, which is laborious and expensive to obtain. In this paper, we propose a weakly supervised 2D se…
Uncertainty Inspired RGB-D Saliency Detection
Jing Zhang, Deng-Ping Fan, Yuchao Dai +4
We propose the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection models tr…
Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection
Jing Zhang, Jianwen Xie, Nick Barnes
In this paper, we propose a noise-aware encoder-decoder framework to disentangle a clean saliency predictor from noisy training examples, where the noisy labels are generated by un…
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Jing Zhang, Deng-Ping Fan, Yuchao Dai +4
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detecti…
Weakly-Supervised Salient Object Detection via Scribble Annotations
Jing Zhang, Xin Yu, Aixuan Li +3
Compared with laborious pixel-wise dense labeling, it is much easier to label data by scribbles, which only costs 12 seconds to label one image. However, using scribble label…