Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Li Liu

Li Liu

University of Oulu, National University of Defense Technology

WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking


Jan 19, 2022
Chunhui Zhang, Guanjie Huang, Li Liu, Shan Huang, Yinan Yang, Yuxuan Zhang, Xiang Wan, Shiming Ge


  Access Paper or Ask Questions

Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery


Jan 19, 2022
Yan Zhao, Lingjun Zhao, Zhong Liu, Dewen Hu, Gangyao Kuang, Li Liu

* A raw version as the same as the early access published in TGRS. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses 
  Access Paper or Ask Questions

Improved Neural Distinguishers with (Related-key) Differentials: Applications in SIMON and SIMECK


Jan 11, 2022
Jinyu Lu, Guoqiang Liu, Yunwen Liu, Bing Sun, Chao Li, Li Liu

Figure 1 for Improved Neural Distinguishers with (Related-key) Differentials: Applications in SIMON and SIMECK
Figure 2 for Improved Neural Distinguishers with (Related-key) Differentials: Applications in SIMON and SIMECK
Figure 3 for Improved Neural Distinguishers with (Related-key) Differentials: Applications in SIMON and SIMECK
Figure 4 for Improved Neural Distinguishers with (Related-key) Differentials: Applications in SIMON and SIMECK

  Access Paper or Ask Questions

Decoupling Makes Weakly Supervised Local Feature Better


Jan 08, 2022
Kunhong Li, LongguangWang, Li Liu, Qing Ran, Kai Xu, Yulan Guo

Figure 1 for Decoupling Makes Weakly Supervised Local Feature Better
Figure 2 for Decoupling Makes Weakly Supervised Local Feature Better
Figure 3 for Decoupling Makes Weakly Supervised Local Feature Better
Figure 4 for Decoupling Makes Weakly Supervised Local Feature Better

  Access Paper or Ask Questions

MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution


Jan 06, 2022
Xinyi Ying, Yingqian Wang, Longguang Wang, Weidong Sheng, Li Liu, Zaiping Lin, Shilin Zhou

Figure 1 for MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution
Figure 2 for MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution
Figure 3 for MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution
Figure 4 for MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution

  Access Paper or Ask Questions

Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors


Nov 22, 2021
Zihan Yan, Li Liu, Xin Li, William K. Cheung, Youmin Zhang, Qun Liu, Guoyin Wang

Figure 1 for Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Figure 2 for Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Figure 3 for Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Figure 4 for Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
* IEEE Transactions on Knowledge and Data Engineering 2021 
  Access Paper or Ask Questions

Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework


Nov 10, 2021
Keyu Li, Yangxin Xu, Jian Wang, Dong Ni, Li Liu, Max Q. -H. Meng

Figure 1 for Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework
Figure 2 for Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework
Figure 3 for Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework
Figure 4 for Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework
* Accepted by IEEE Transactions on Medical Robotics and Bionics. Copyright may be transferred without notice, after which this version may no longer be accessible 
  Access Paper or Ask Questions

Unsupervised cross-user adaptation in taste sensationrecognition based on surface electromyography withconformal prediction and domain regularizedcomponent analysis


Oct 20, 2021
Hengyang Wang, Xianghao Zhan, Li Liu, Asif Ullah, Huiyan Li, Han Gao, You Wang, Guang Li

Figure 1 for Unsupervised cross-user adaptation in taste sensationrecognition based on surface electromyography withconformal prediction and domain regularizedcomponent analysis
Figure 2 for Unsupervised cross-user adaptation in taste sensationrecognition based on surface electromyography withconformal prediction and domain regularizedcomponent analysis
Figure 3 for Unsupervised cross-user adaptation in taste sensationrecognition based on surface electromyography withconformal prediction and domain regularizedcomponent analysis
Figure 4 for Unsupervised cross-user adaptation in taste sensationrecognition based on surface electromyography withconformal prediction and domain regularizedcomponent analysis

  Access Paper or Ask Questions

Dynamic Binary Neural Network by learning channel-wise thresholds


Oct 08, 2021
Jiehua Zhang, Zhuo Su, Yanghe Feng, Xin Lu, Matti Pietikäinen, Li Liu

Figure 1 for Dynamic Binary Neural Network by learning channel-wise thresholds
Figure 2 for Dynamic Binary Neural Network by learning channel-wise thresholds
Figure 3 for Dynamic Binary Neural Network by learning channel-wise thresholds
Figure 4 for Dynamic Binary Neural Network by learning channel-wise thresholds
* 5 pages, 3 figures 
  Access Paper or Ask Questions

J-Score: A Robust Measure of Clustering Accuracy


Sep 03, 2021
Navid Ahmadinejad, Li Liu

Figure 1 for J-Score: A Robust Measure of Clustering Accuracy
Figure 2 for J-Score: A Robust Measure of Clustering Accuracy
Figure 3 for J-Score: A Robust Measure of Clustering Accuracy

  Access Paper or Ask Questions

Pixel Difference Networks for Efficient Edge Detection


Aug 16, 2021
Zhuo Su, Wenzhe Liu, Zitong Yu, Dewen Hu, Qing Liao, Qi Tian, Matti Pietikäinen, Li Liu

Figure 1 for Pixel Difference Networks for Efficient Edge Detection
Figure 2 for Pixel Difference Networks for Efficient Edge Detection
Figure 3 for Pixel Difference Networks for Efficient Edge Detection
Figure 4 for Pixel Difference Networks for Efficient Edge Detection
* ICCV 2021 
  Access Paper or Ask Questions

Investigate the Essence of Long-Tailed Recognition from a Unified Perspective


Jul 08, 2021
Lei Liu, Li Liu

Figure 1 for Investigate the Essence of Long-Tailed Recognition from a Unified Perspective
Figure 2 for Investigate the Essence of Long-Tailed Recognition from a Unified Perspective
Figure 3 for Investigate the Essence of Long-Tailed Recognition from a Unified Perspective
Figure 4 for Investigate the Essence of Long-Tailed Recognition from a Unified Perspective

  Access Paper or Ask Questions

Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition


Jun 25, 2021
Jianrong Wang, Ziyue Tang, Xuewei Li, Mei Yu, Qiang Fang, Li Liu

Figure 1 for Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition
Figure 2 for Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition
Figure 3 for Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition
Figure 4 for Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition

  Access Paper or Ask Questions

Multi-modal Entity Alignment in Hyperbolic Space


Jun 07, 2021
Hao Guo, Jiuyang Tang, Weixin Zeng, Xiang Zhao, Li Liu

Figure 1 for Multi-modal Entity Alignment in Hyperbolic Space
Figure 2 for Multi-modal Entity Alignment in Hyperbolic Space
Figure 3 for Multi-modal Entity Alignment in Hyperbolic Space
Figure 4 for Multi-modal Entity Alignment in Hyperbolic Space
* 24 pages,5 figures; 
  Access Paper or Ask Questions

Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification


Apr 30, 2021
Yichao Yan, Jie Qin1, Jiaxin Chen, Li Liu, Fan Zhu, Ying Tai, Ling Shao

Figure 1 for Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
Figure 2 for Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
Figure 3 for Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
Figure 4 for Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
* CVPR 2020 
  Access Paper or Ask Questions

Learning Multi-Attention Context Graph for Group-Based Re-Identification


Apr 29, 2021
Yichao Yan, Jie Qin, Bingbing Ni, Jiaxin Chen, Li Liu, Fan Zhu, Wei-Shi Zheng, Xiaokang Yang, Ling Shao

Figure 1 for Learning Multi-Attention Context Graph for Group-Based Re-Identification
Figure 2 for Learning Multi-Attention Context Graph for Group-Based Re-Identification
Figure 3 for Learning Multi-Attention Context Graph for Group-Based Re-Identification
Figure 4 for Learning Multi-Attention Context Graph for Group-Based Re-Identification

  Access Paper or Ask Questions

Anchor-Free Person Search


Mar 22, 2021
Yichao Yan, Jingpeng Li, Jie Qin, Song Bai, Shengcai Liao, Li Liu, Fan Zhu, Ling Shao

Figure 1 for Anchor-Free Person Search
Figure 2 for Anchor-Free Person Search
Figure 3 for Anchor-Free Person Search
Figure 4 for Anchor-Free Person Search
* Accepted by CVPR 2021 
  Access Paper or Ask Questions

Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning


Mar 01, 2021
Keyu Li, Jian Wang, Yangxin Xu, Hao Qin, Dongsheng Liu, Li Liu, Max Q. -H. Meng

Figure 1 for Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
Figure 2 for Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
Figure 3 for Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
Figure 4 for Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
* Accepted at ICRA 2021. Copyright may be transferred without notice, after which this version may no longer be accessible 
  Access Paper or Ask Questions

Deep Learning for Scene Classification: A Survey


Feb 20, 2021
Delu Zeng, Minyu Liao, Mohammad Tavakolian, Yulan Guo, Bolei Zhou, Dewen Hu, Matti Pietikäinen, Li Liu

Figure 1 for Deep Learning for Scene Classification: A Survey
Figure 2 for Deep Learning for Scene Classification: A Survey
Figure 3 for Deep Learning for Scene Classification: A Survey
Figure 4 for Deep Learning for Scene Classification: A Survey

  Access Paper or Ask Questions

Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose


Feb 05, 2021
Li Liu, Xianghao Zhan, Rumeng Wu, Xiaoqing Guan, Zhan Wang, Wei Zhang, You Wang, Zhiyuan Luo, Guang Li

Figure 1 for Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose
Figure 2 for Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose
Figure 3 for Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose
Figure 4 for Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose

  Access Paper or Ask Questions

Deep Image Retrieval: A Survey


Feb 03, 2021
Wei Chen, Yu Liu, Weiping Wang, Erwin Bakker, Theodoros Georgiou, Paul Fieguth, Li Liu, Michael S. Lew

Figure 1 for Deep Image Retrieval: A Survey
Figure 2 for Deep Image Retrieval: A Survey
Figure 3 for Deep Image Retrieval: A Survey
Figure 4 for Deep Image Retrieval: A Survey
* 20 pages, 11 figures 
  Access Paper or Ask Questions

How to Train Your Agent to Read and Write


Jan 04, 2021
Li Liu, Mengge He, Guanghui Xu, Mingkui Tan, Qi Wu

Figure 1 for How to Train Your Agent to Read and Write
Figure 2 for How to Train Your Agent to Read and Write
Figure 3 for How to Train Your Agent to Read and Write
Figure 4 for How to Train Your Agent to Read and Write

  Access Paper or Ask Questions

Effective Sample Pair Generation for Ultrasound Video Contrastive Representation Learning


Nov 25, 2020
Yixiong Chen, Chunhui Zhang, Li Liu, Cheng Feng, Changfeng Dong, Yongfang Luo, Xiang Wan

Figure 1 for Effective Sample Pair Generation for Ultrasound Video Contrastive Representation Learning
Figure 2 for Effective Sample Pair Generation for Ultrasound Video Contrastive Representation Learning
Figure 3 for Effective Sample Pair Generation for Ultrasound Video Contrastive Representation Learning
Figure 4 for Effective Sample Pair Generation for Ultrasound Video Contrastive Representation Learning

  Access Paper or Ask Questions

Towards Class-Specific Unit


Nov 22, 2020
Runkai Zheng, Zhijia Yu, Yinqi Zhang, Chris Ding, Hei Victor Cheng, Li Liu

Figure 1 for Towards Class-Specific Unit
Figure 2 for Towards Class-Specific Unit
Figure 3 for Towards Class-Specific Unit
Figure 4 for Towards Class-Specific Unit

  Access Paper or Ask Questions

A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges


Nov 17, 2020
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi

Figure 1 for A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Figure 2 for A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Figure 3 for A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Figure 4 for A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

  Access Paper or Ask Questions