4 citations · 13 across the 17 of their papers we have counts for
17 papers
Universal Learned Image Compression With Low Computational Cost
Bowen Li, Yao Xin, Youneng Bao +3
Recently, learned image compression methods have developed rapidly and exhibited excellent rate-distortion performance when compared to traditional standards, such as JPEG, JPEG200…
Context-aware Visual Tracking with Joint Meta-updating
Qiuhong Shen, Xin Li, Fanyang Meng +1
Visual object tracking acts as a pivotal component in various emerging video applications. Despite the numerous developments in visual tracking, existing deep trackers are still li…
Exploring Structural Sparsity in Neural Image Compression
Shanzhi Yin, Chao Li, Wen Tan +3
Neural image compression have reached or out-performed traditional methods (such as JPEG, BPG, WebP). However,their sophisticated network structures with cascaded convolution layer…
Transformations in Learned Image Compression from a Modulation Perspective
Youneng Bao, Fangyang Meng, Wen Tan +3
In this paper, a unified transformation method in learned image compression(LIC) is proposed from the perspective of modulation. Firstly, the quantization in LIC is considered as a…
Surrogate-assisted cooperative signal optimization for large-scale traffic networks
Yongsheng Liang, Zhigang Ren, Lin Wang +2
Reasonable setting of traffic signals can be very helpful in alleviating congestion in urban traffic networks. Meta-heuristic optimization algorithms have proved themselves to be a…
Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection
Xiaodong Ren, Daofu Guo, Zhigang Ren +2
By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving compu…
A Surrogate-Assisted Variable Grouping Algorithm for General Large Scale Global Optimization Problems
An Chen, Zhigang Ren, Muyi Wang +3
Problem decomposition plays a vital role when applying cooperative coevolution (CC) to large scale global optimization problems. However, most learning-based decomposition algorith…
TCDesc: Learning Topology Consistent Descriptors
Honghu Pan, Fanyang Meng, Zhenyu He +2
Triplet loss is widely used for learning local descriptors from image patch. However, triplet loss only minimizes the Euclidean distance between matching descriptors and maximizes…
An Eigenspace Divide-and-Conquer Approach for Large-Scale Optimization
Zhigang Ren, Yongsheng Liang, Muyi Wang +2
Divide-and-conquer-based (DC-based) evolutionary algorithms (EAs) have achieved notable success in dealing with large-scale optimization problems (LSOPs). However, the appealing pe…
A Fast Differential Grouping Algorithm for Large Scale Black-Box Optimization
Zhigang Ren, An Chen, Yaochu Jin +3
Decomposition plays a significant role in cooperative co-evolution which shows great potential in large scale black-box optimization. However, current popular decomposition algorit…
Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering
Yongsheng Liang, Zhigang Ren, Bei Pang +1
As a model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied to global optimization. However, tr…
Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models
Bei Pang, Zhigang Ren, Yongsheng Liang +1
It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performa…
A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems
An Chen, Yipeng Zhang, Zhigang Ren +2
By taking the idea of divide-and-conquer, cooperative coevolution (CC) provides a powerful architecture for large scale global optimization (LSGO) problems, but its efficiency reli…
Surrogate Model Assisted Cooperative Coevolution for Large Scale Optimization
Zhigang Ren, Bei Pang, Yongsheng Liang +2
It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performa…
Boosting Cooperative Coevolution for Large Scale Optimization with a Fine-Grained Computation Resource Allocation Strategy
Zhigang Ren, Yongsheng Liang, Aimin Zhang +3
Cooperative coevolution (CC) has shown great potential in solving large scale optimization problems (LSOPs). However, traditional CC algorithms often waste part of computation reso…
Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive
Yongsheng Liang, Zhigang Ren, Xianghua Yao +2
As a typical model-based evolutionary algorithm (EA), estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied to global optimization…
A Bidirectional Adaptive Bandwidth Mean Shift Strategy for Clustering
Fanyang Meng, Hong Liu, Yongsheng Liang +2
The bandwidth of a kernel function is a crucial parameter in the mean shift algorithm. This paper proposes a novel adaptive bandwidth strategy which contains three main contributio…