571 citations · 3.6k across the 80 of their papers we have counts for
7 papers · 1 filter
Dual Deep Network for Visual Tracking
Zhizhen Chi, Hongyang Li, Huchuan Lu +1
Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we…
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Chao Ma, Chih-Yuan Yang, Xiaokang Yang +1
Numerous single-image super-resolution algorithms have been proposed in the literature, but few studies address the problem of performance evaluation based on visual perception. Wh…
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution
Jiawei Zhang, Jinshan Pan, Wei-Sheng Lai +2
In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image decon…
Superpixel Hierarchy
Xing Wei, Qingxiong Yang, Yihong Gong +2
Superpixel segmentation is becoming ubiquitous in computer vision. In practice, an object can either be represented by a number of segments in finer levels of detail or included in…
Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
Jimei Yang, Brian Price, Scott Cohen +2
We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Different from previous low-level edge detection, our algorithm focus…
Learning Support Correlation Filters for Visual Tracking
Wangmeng Zuo, Xiaohe Wu, Liang Lin +2
Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency. Re…
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Jimei Yang, Scott Reed, Ming-Hsuan Yang +1
An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability…