35 citations · 96 across the 20 of their papers we have counts for
13 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…
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds
Sameera Ramasinghe, Moshiur Farazi, Salman Khan +2
Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output m…
Conditional Generative Modeling via Learning the Latent Space
Sameera Ramasinghe, Kanchana Ranasinghe, Salman Khan +2
Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-mod…
Attention Guided Semantic Relationship Parsing for Visual Question Answering
Moshiur Farazi, Salman Khan, Nick Barnes
Humans explain inter-object relationships with semantic labels that demonstrate a high-level understanding required to perform complex Vision-Language tasks such as Visual Question…
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…