85 citations · 273 across the 13 of their papers we have counts for
9 papers · 1 filter
MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution
Dong Gong, Mingkui Tan, Qinfeng Shi +2
Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, ho…
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
Lei Zhang, Peng Wang, Chunhua Shen +4
Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their applicatio…
Learning Deep Gradient Descent Optimization for Image Deconvolution
Dong Gong, Zhen Zhang, Qinfeng Shi +3
As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed natur…
FVQA: Fact-based Visual Question Answering
Peng Wang, Qi Wu, Chunhua Shen +2
Visual Question Answering (VQA) has attracted a lot of attention in both Computer Vision and Natural Language Processing communities, not least because it offers insight into the r…
Pushing the Limits of Deep CNNs for Pedestrian Detection
Qichang Hu, Peng Wang, Chunhua Shen +2
Compared to other applications in computer vision, convolutional neural networks have under-performed on pedestrian detection. A breakthrough was made very recently by using sophis…
Exploring Context with Deep Structured models for Semantic Segmentation
Guosheng Lin, Chunhua Shen, Anton van den Hengel +1
State-of-the-art semantic image segmentation methods are mostly based on training deep convolutional neural networks (CNNs). In this work, we proffer to improve semantic segmentati…
Efficient piecewise training of deep structured models for semantic segmentation
Guosheng Lin, Chunhua Shen, Anton van dan Hengel +1
Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through…
Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications
Peng Wang, Chunhua Shen, Anton van den Hengel +1
In computer vision, many problems such as image segmentation, pixel labelling, and scene parsing can be formulated as binary quadratic programs (BQPs). For submodular problems, cut…
Characterness: An Indicator of Text in the Wild
Yao Li, Wenjing Jia, Chunhua Shen +1
Text in an image provides vital information for interpreting its contents, and text in a scene can aide with a variety of tasks from navigation, to obstacle avoidance, and odometry…