Xialei Liu 刘夏雷
Xialei Liu is an associate professor at Nankai University. Before that, He was a postdoctoral researcher at the University of Edinburgh working with Prof. Hakan Bilen. He received his Master and PhD degrees from the Autonomous University of Barcelona, supervised by Prof. Joost van de Weijer and Prof. Andrew D. Bagdanov. He received a second Master degree and a Bachelor degree at Northwestern Polytechnical University. He works in the field of computer vision and machine learning. His research interests include lifelong learning, self-supervised learning, few-shot learning, long-tailed learning, and many applications (classification, detection, segmentation, crowd counting, image quality assessment, etc).
Google scholar, Linkedin, ResearchGate, Github.
刘夏雷，现任南开大学副教授，入选南开大学“百名青年学科带头人培养计划”，中国科协青年托举计划，博士生导师。在英国爱丁堡大学从事博士后研究，合作导师： Prof. Hakan Bilen。获得西班牙巴塞罗那自治大学计算机科学博士学位，博士导师：Prof. Joost van de Weijer 和 Prof. Andrew D. Bagdanov。本科、硕士毕业于西北工业大学自动化学院，硕士导师程咏梅教授。 主要研究领域为计算机视觉和模式识别，特别是开放环境下机器学习及应用，主要研究方向包括自监督学习、连续学习、小样本学习、长尾分布学习等，并在多种应用中验证（图像识别、检测、分割、人群计数、图像质量评价等）。
News
- 2 papers accepted at ECCV 2024
- 2 papers accepted at CVPR 2024
- 第二届粤港澳大湾区(黄埔)国际算法算例大赛“序列任务的持续学习” 冠军
- 3 papers accepted at ICCV 2023
- 1 paper accepted at IJCV, 2023.07
- 邀请报告：CCIG2023知识引导的自适应感知与结构理解技术论坛（知识引导的连续学习方法探究）
- 1 paper accepted at CVPR 2023 (Continual Semantic Segmentation)
- Served as CVPR 2023 4th CLVISION Workshop organizer (Continual Learning)
- Invited talk at ICCPR 2022 (link)
- 1 paper accepted at TPAMI 2022.10 (arXiv: Survey on Class-incremental Learning, IEEE TPAMI online)
- 1 paper accepted at ECCV 2022.10 (Long-tailed Class-incremental Learning)
- Teaching： Computer Vision 计算机视觉 (研究生)- 2022 秋
- Teaching： Global Outlook and Personal Development 国际视野与个人成长 (本科生)- 2022 秋 （英）
- CSIG交通视频专委会连续学习前沿论坛 (论坛链接，直播回放)
- 1 paper selected as CVPR 2022 Best Paper Finalists (Multi-task Learning)
- VALSE 2022.08 Tutorial Invited Talk (连续学习 / PPT链接 [提取码：e93u])
- 3 papers accepted at CVPR 2022.06 (Continual learning on Segmentation, Few-shot, Multi-task Learning)
- 1 paper accepted at TNNLS, 2022.02 (Continual learning on Segmentation)
- VALSE 2022.08 组委会成员注册主席 (Registration Chair)
- VALSE 2022.08 Workshop 组织者成员 (开放环境下机器学习及应用)
- Teaching： Artificial Intelligence 人工智能导论 (本科生)- 2022 春
Recent Publications
Notes: Joint first authors are indicated using # and corresponding authors are indicated using *.
- Generative Multi-modal Models are Good Class-Incremental Learners, X Cao, H Lu, L Huang, X Liu*, MM Cheng, CVPR 2024.
- Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning, X Liu#, JT Zhai#, AD Bagdanov, K Li, MM Cheng*, CVPR 2024.
- Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental Learning, JT Zhai, X Liu*, L Yu, MM Cheng, AAAI 2024.
- Masked Autoencoders are Efficient Class Incremental Learners, JT Zhai, X Liu*, AD Bagdanov, K Li, MM Cheng, ICCV 2023.
- Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising, X Jin#, JW Xiao#, LH Han, C Guo, R Zhang, X Liu, C Li, ICCV 2023.
- Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection, Y Liu, Y Cong, D Goswami, X Liu, J Van de Weijer, ICCV 2023.
- Endpoints Weight Fusion for Class Incremental Semantic Segmentation, JW Xiao#, CB Zhang#, J Feng, X Liu*, J van de Weijer, MM Cheng, CVPR 2023
- Universal Representations: A Unified Look at Multiple Task and Domain Learning, WH Li, X Liu, H Bilen, IJCV, 2023.
- Class-incremental Learning: Survey and Performance Evaluation, M Masana, X Liu*, B Twardowski, M Menta, et al., IEEE TPAMI, 2022.
- Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification, K Wang#, C Wu#, AD Bagdanov, X Liu*, et al., BMVC 2022.
- Long-Tailed Class Incremental Learning, X Liu*,#, YS Hu#, XS Cao, AD Bagdanov, K Li, MM Cheng, ECCV 2022.
- Representation Compensation Networks for Continual Semantic Segmentation, CB Zhang, JW Xiao, X Liu*, YC Chen, MM Cheng, IEEE CVPR 2022.
- Improving Task Adaptation for Cross-domain Few-shot Learning, WH Li, X Liu*, H Bilen, IEEE CVPR, 2022.
- Learning Multiple Dense Prediction Tasks from Partially Annotated Data, WH Li, X Liu, H Bilen, IEEE CVPR, 2022.
- Self-Training for Class-Incremental Semantic Segmentation, L Yu, X Liu*, J van de Weijer, TNNLS, 2022.
- HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification, K Wang, X Liu*, L Herranz, J Van de Weijer, BMVC, 2021.
- Universal Representation Learning from Multiple Domains for Few-shot Classification, WH Li, X Liu*, H Bilen, IEEE ICCV, 2021.
- Semantic Drift Compensation for Class-Incremental Learning, L Yu, B Twardowski, X Liu, et al., IEEE CVPR, 2020.
- Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank, X Liu, J van de Weijer, AD Bagdanov, IEEE TPAMI, 2019.
- Learning Metrics from Teachers: Compact Networks for Image Embedding, L Yu, VO Yazici, X Liu, J van de Weijer, et al., IEEE CVPR, 2019.
- Memory Replay GANs: Learning to Generate Images from New Categories without Forgetting, C Wu, L Herranz, X Liu, et al., NeurIPS, 2018.
- Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting, X Liu#, M Masana#, L Herranz, et al., ICPR, 2018.
- Leveraging Unlabeled Data for Crowd Counting by Learning to Rank, X Liu, J van de Weijer, AD Bagdanov, IEEE CVPR, 2018.
- Rankiqa: Learning from Rankings for No-reference Image Quality Assessment, X Liu, J van de Weijer, AD Bagdanov, IEEE ICCV, 2017.
Useful links
Contact
- Email: xialei AT nankai DOT edu DOT cn
- Office: 450, College of Computer Science, Nankai University