269 citations · 1.8k across the 57 of their papers we have counts for
8 papers · 1 filter
DNQ: Dynamic Network Quantization
Yuhui Xu, Shuai Zhang, Yingyong Qi +3
Network quantization is an effective method for the deployment of neural networks on memory and energy constrained mobile devices. In this paper, we propose a Dynamic Network Quant…
Trained Rank Pruning for Efficient Deep Neural Networks
Yuhui Xu, Yuxi Li, Shuai Zhang +6
The performance of Deep Neural Networks (DNNs) keeps elevating in recent years with increasing network depth and width. To enable DNNs on edge devices like mobile phones, researche…
Network Decoupling: From Regular to Depthwise Separable Convolutions
Jianbo Guo, Yuxi Li, Weiyao Lin +2
Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first anal…
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
Yuxi Li, Jiuwei Li, Weiyao Lin +1
Object detection has made great progress in the past few years along with the development of deep learning. However, most current object detection methods are resource hungry, whic…
Enhancing HEVC Compressed Videos with a Partition-masked Convolutional Neural Network
Xiaoyi He, Qiang Hu, Xintong Han +3
In this paper, we propose a partition-masked Convolution Neural Network (CNN) to achieve compressed-video enhancement for the state-of-the-art coding standard, High Efficiency Vide…
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Huai-Qian Khor, John See, Raphael C. W. Phan +1
Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved…
Deep Neural Network Compression with Single and Multiple Level Quantization
Yuhui Xu, Yongzhuang Wang, Aojun Zhou +2
Network quantization is an effective solution to compress deep neural networks for practical usage. Existing network quantization methods cannot sufficiently exploit the depth info…
Unsupervised Deep Domain Adaptation for Pedestrian Detection
Lihang Liu, Weiyao Lin, Lisheng Wu +2
This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes. First, we utilize an iterative algorithm to iteratively se…