L
Li Liu
Researcher at University of East Anglia
Publications - 94
Citations - 5733
Li Liu is an academic researcher from University of East Anglia. The author has contributed to research in topics: Hash function & Feature hashing. The author has an hindex of 34, co-authored 92 publications receiving 4220 citations. Previous affiliations of Li Liu include Southwest University & University of Sheffield.
Papers
More filters
Journal ArticleDOI
Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization
TL;DR: This work proposes a simple yet effective unsupervised hashing framework, named Similarity-Adaptive Deep Hashing (SADH), which alternatingly proceeds over three training modules: deep hash model training, similarity graph updating and binary code optimization.
Journal ArticleDOI
Feature Learning for Image Classification Via Multiobjective Genetic Programming
Ling Shao,Li Liu,Xuelong Li +2 more
TL;DR: Experimental results verify that the proposed evolutionary learning methodology significantly outperforms many state-of-the-art hand-designed features and two feature learning techniques in terms of classification accuracy.
Proceedings Article
Learning discriminative representations from RGB-D video data
TL;DR: This paper introduces an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGB-D video data for visual recognition tasks with significant advantages compared with state-of-the-art hand-crafted and machine-learned features.
Proceedings ArticleDOI
Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval
TL;DR: This paper introduces a novel binary coding method, named Deep Sketch Hashing (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework, and is the first hashing work specifically designed for category-level SBIR with an end to end deep architecture.
Proceedings ArticleDOI
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning
Ji Zhang,Yu Liu,Ke Zhou,Guoliang Li,Zhili Xiao,Bin Cheng,Jiashu Xing,Yangtao Wang,Tianheng Cheng,Li Liu,Minwei Ran,Zekang Li +11 more
TL;DR: An end-to-end automatic CDB tuning system, CDBTune, using deep reinforcement learning (RL), which enables end- to-end learning and accelerates the convergence speed of the model and improves efficiency of online tuning.