activity
20192021
most citedSelf-Weighted Robust LDA for Multiclass Classification with Edge Classes

9 citations · 18 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV20213 cited

DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers

Changlin Li, Guangrun Wang, Bing Wang +3

Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference. However, their…

cs.CV20211 cited

Dynamic Slimmable Network

Changlin Li, Guangrun Wang, Bing Wang +3

Current dynamic networks and dynamic pruning methods have shown their promising capability in reducing theoretical computation complexity. However, dynamic sparse patterns on convo…

cs.CV20212 cited

NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition

Pengzhen Ren, Gang Xiao, Xiaojun Chang +3

In the field of complex action recognition in videos, the quality of the designed model plays a crucial role in the final performance. However, artificially designed network struct…

cs.LG20209 cited

Self-Weighted Robust LDA for Multiclass Classification with Edge Classes

Caixia Yan, Xiaojun Chang, Minnan Luo +4

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are p…

cs.LG2020

A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions

Pengzhen Ren, Yun Xiao, Xiaojun Chang +4

Deep learning has made breakthroughs and substantial in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is…

cs.IR20193 cited

Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval

Lei Zhu, Zi Huang, Zhihui Li +2

Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency.…