94 Papers
560 Citations
Li Liu is an academic researcher from University of East Anglia. The author has contributed to research in topics: Computer science & Hash function. The author has an hindex of 34, co-authored 92 publications. Previous affiliations of Li Liu include Southwest University & University of Sheffield.
Chat about Author
Papers
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.
426
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
- 25 Jun 2019
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.
328
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.
322
Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval
Li Liu,Fumin Shen,Yuming Shen,Xianglong Liu,Ling Shao +4 more
- 21 Jul 2017
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.
RANet: Ranking Attention Network for Fast Video Object Segmentation.
TL;DR: This paper proposes a novel ranking attention module, which automatically ranks and selects these maps for fine-grained VOS performance, and develops a real-time yet very accurate Ranking Attention Network (RANet) for VOS.
262