427 citations · 1.1k across the 82 of their papers we have counts for
13 papers · 1 filter
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…
Binarized Neural Architecture Search
Hanlin Chen, Li'an Zhuo, Baochang Zhang +4
Neural architecture search (NAS) can have a significant impact in computer vision by automatically designing optimal neural network architectures for various tasks. A variant, bina…
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…
Semantic-aware Image Deblurring
Fuhai Chen, Rongrong Ji, Chengpeng Dai +6
Image deblurring has achieved exciting progress in recent years. However, traditional methods fail to deblur severely blurred images, where semantic contents appears ambiguously. I…
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…
Bayesian Optimized 1-Bit CNNs
Jiaxin Gu, Junhe Zhao, Xiaolong Jiang +4
Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great cha…
Interpretable Neural Network Decoupling
Yuchao Li, Rongrong Ji, Shaohui Lin +5
The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the e…
Supervised Online Hashing via Similarity Distribution Learning
Mingbao Lin, Rongrong Ji, Shen Chen +6
Online hashing has attracted extensive research attention when facing streaming data. Most online hashing methods, learning binary codes based on pairwise similarities of training…
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning
Xiawu Zheng, Chenyi Yang, Shaokun Zhang +6
Neural Architecture Search (NAS) has demonstrated state-of-the-art performance on various computer vision tasks. Despite the superior performance achieved, the efficiency and gener…
Multinomial Distribution Learning for Effective Neural Architecture Search
Xiawu Zheng, Rongrong Ji, Lang Tang +3
Architectures obtained by Neural Architecture Search (NAS) have achieved highly competitive performance in various computer vision tasks. However, the prohibitive computation deman…
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network
Xiaolong Jiang, Zehao Xiao, Baochang Zhang +4
Crowd counting has recently attracted increasing interest in computer vision but remains a challenging problem. In this paper, we propose a trellis encoder-decoder network (TEDnet)…
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
Shaohui Lin, Rongrong Ji, Chenqian Yan +5
Structured pruning of filters or neurons has received increased focus for compressing convolutional neural networks. Most existing methods rely on multi-stage optimizations in a la…