35 citations · 63 across the 6 of their papers we have counts for
11 papers
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
Yang Liu, Zhenyue Qin, Saeed Anwar +2
Ever since the advent of AlexNet, designing novel deep neural architectures for different tasks has consistently been a productive research direction. Despite the exceptional perfo…
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…
Identity Enhanced Residual Image Denoising
Saeed Anwar, Cong Phuoc Huynh, Fatih Porikli
We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising. Our netw…
Mosaic Super-resolution via Sequential Feature Pyramid Networks
Mehrdad Shoeiby, Mohammad Ali Armin, Sadegh Aliakbarian +2
Advances in the design of multi-spectral cameras have led to great interests in a wide range of applications, from astronomy to autonomous driving. However, such cameras inherently…
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…
Attention Based Real Image Restoration
Saeed Anwar, Nick Barnes, Lars Petersson
Deep convolutional neural networks perform better on images containing spatially invariant degradations, also known as synthetic degradations; however, their performance is limited…