1k citations · 2k across the 41 of their papers we have counts for
8 papers · 2 filters
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…
Image Colorization: A Survey and Dataset
Saeed Anwar, Muhammad Tahir, Chongyi Li +3
Image colorization estimates RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality. Over the last decade, deep learning techniques for i…
Dense-Resolution Network for Point Cloud Classification and Segmentation
Shi Qiu, Saeed Anwar, Nick Barnes
Point cloud analysis is attracting attention from Artificial Intelligence research since it can be widely used in applications such as robotics, Augmented Reality, self-driving. Ho…
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…
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…
A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers
Saeed Anwar, Nick Barnes, Lars Petersson
To make the best use of the underlying minute and subtle differences, fine-grained classifiers collect information about inter-class variations. The task is very challenging due to…