30 citations · 46 across the 5 of their papers we have counts for
6 papers
Semi-supervised Multi-task Learning for Semantics and Depth
Yufeng Wang, Yi-Hsuan Tsai, Wei-Chih Hung +3
Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance. Typical MTL methods are jointly trained…
Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting
Xiyang Liu, Jie Yang, Wenrui Ding
The crowd counting task aims at estimating the number of people located in an image or a frame from videos. Existing methods widely adopt density maps as the training targets to op…
GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
Chunlei Liu, Wenrui Ding, Yuan Hu +3
Training 1-bit deep convolutional neural networks (DCNNs) is one of the most challenging problems in computer vision, because it is much easier to get trapped into local minima tha…
Aggregation Signature for Small Object Tracking
Chunlei Liu, Wenrui Ding, Jinyu Yang +4
Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small…
Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagation
Chunlei Liu, Wenrui Ding, Xin Xia +5
The rapidly decreasing computation and memory cost has recently driven the success of many applications in the field of deep learning. Practical applications of deep learning in re…
RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
Chunlei Liu, Wenrui Ding, Xin Xia +5
Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips b…