Xialei Liu

According to our database, Xialei Liu authored at least 37 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Generative Multi-modal Models are Good Class-Incremental Learners.

Xu-Sheng Cao, Haori Lu, Linlan Huang, Xialei Liu, Ming-Ming Cheng

CoRR, 2024

GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery.

Enguang Wang, Zhimao Peng, Zhengyuan Xie, Xialei Liu, Ming-Ming Cheng

CoRR, 2024

Fine-Grained Knowledge Selection and Restoration for Non-exemplar Class Incremental Learning.

Jiang-Tian Zhai, Xialei Liu, Lu Yu, Ming-Ming Cheng

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Sequential interactive image segmentation.

Zheng Lin, Zhao Zhang, Ziyue Zhu, Deng-Ping Fan, Xialei Liu

Comput. Vis. Media, December, 2023

Self-Training for Class-Incremental Semantic Segmentation.

Lu Yu, Xialei Liu, Joost van de Weijer

IEEE Trans. Neural Networks Learn. Syst., November, 2023

Class-Incremental Learning: Survey and Performance Evaluation on Image Classification.

Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, Joost van de Weijer

IEEE Trans. Pattern Anal. Mach. Intell., May, 2023

Class Incremental Learning with Pre-trained Vision-Language Models.

Xialei Liu, Xu-Sheng Cao, Haori Lu, Jia-Wen Xiao, Andrew D. Bagdanov, Ming-Ming Cheng

CoRR, 2023

Masked Autoencoders are Efficient Class Incremental Learners.

Jiang-Tian Zhai, Xialei Liu, Andrew D. Bagdanov, Ke Li, Ming-Ming Cheng

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection.

Yuyang Liu, Yang Cong, Dipam Goswami, Xialei Liu, Joost van de Weijer

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Lighting Every Darkness in Two Pairs : A Calibration-Free Pipeline for RAW Denoising.

Xin Jin, Jia-Wen Xiao, Linghao Han, Chunle Guo, Ruixun Zhang, Xialei Liu, Chongyi Li

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Endpoints Weight Fusion for Class Incremental Semantic Segmentation.

Jia-Wen Xiao, Chang-Bin Zhang, Jiekang Feng, Xialei Liu, Joost van de Weijer, Ming-Ming Cheng

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Robust Saliency Guidance for Data-free Class Incremental Learning.

Xialei Liu, Jiang-Tian Zhai, Andrew D. Bagdanov, Ke Li, Ming-Ming Cheng

CoRR, 2022

Universal Representations: A Unified Look at Multiple Task and Domain Learning.

Wei-Hong Li, Xialei Liu, Hakan Bilen

CoRR, 2022

Long-Tailed Class Incremental Learning.

Xialei Liu, Yu-Song Hu, Xu-Sheng Cao, Andrew D. Bagdanov, Ke Li, Ming-Ming Cheng

Proceedings of the Computer Vision - ECCV 2022, 2022

Representation Compensation Networks for Continual Semantic Segmentation.

Chang-Bin Zhang, Jia-Wen Xiao, Xialei Liu, Ying-Cong Chen, Ming-Ming Cheng

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition.

Kai Wang, Xialei Liu, Andy Bagdanov, Luis Herranz, Shangling Jui, Joost van de Weijer

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Learning Multiple Dense Prediction Tasks from Partially Annotated Data.

Wei-Hong Li, Xialei Liu, Hakan Bilen

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Cross-domain Few-shot Learning with Task-specific Adapters.

Wei-Hong Li, Xialei Liu, Hakan Bilen

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification.

Kai Wang, Chenshen Wu, Andy Bagdanov, Xialei Liu, Shiqi Yang, Shangling Jui, Joost van de Weijer

Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021

Improving Task Adaptation for Cross-domain Few-shot Learning.

Wei-Hong Li, Xialei Liu, Hakan Bilen

CoRR, 2021

Universal Representation Learning from Multiple Domains for Few-shot Classification.

Wei-Hong Li, Xialei Liu, Hakan Bilen

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification.

Kai Wang, Xialei Liu, Luis Herranz, Joost van de Weijer

Proceedings of the 32nd British Machine Vision Conference 2021, 2021

2020

Class-incremental learning: survey and performance evaluation.

Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, Joost van de Weijer

CoRR, 2020

Learning to Rank for Active Learning: A Listwise Approach.

Minghan Li, Xialei Liu, Joost van de Weijer, Bogdan Raducanu

CoRR, 2020

Continual Universal Object Detection.

Xialei Liu, Hao Yang, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto

CoRR, 2020

Learning to Rank for Active Learning: A Listwise Approach.

Minghan Li, Xialei Liu, Joost van de Weijer, Bogdan Raducanu

Proceedings of the 25th International Conference on Pattern Recognition, 2020

Generative Feature Replay For Class-Incremental Learning.

Xialei Liu, Chenshen Wu, Mikel Menta, Luis Herranz, Bogdan Raducanu, Andrew D. Bagdanov, Shangling Jui, Joost van de Weijer

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Semantic Drift Compensation for Class-Incremental Learning.

Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, Joost van de Weijer

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Visual recognition in the wild: learning from rankings in small domains and continual learning in new domains.

Xialei Liu

PhD thesis, 2019

Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank.

Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Learning Metrics From Teachers: Compact Networks for Image Embedding.

Lu Yu, Vacit Oguz Yazici, Xialei Liu, Joost van de Weijer, Yongmei Cheng, Arnau Ramisa

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Memory Replay GANs: learning to generate images from new categories without forgetting.

Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, Bogdan Raducanu

CoRR, 2018

Memory Replay GANs: Learning to Generate New Categories without Forgetting.

Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, Bogdan Raducanu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting.

Xialei Liu, Marc Masana, Luis Herranz, Joost van de Weijer, Antonio M. López, Andrew D. Bagdanov

Proceedings of the 24th International Conference on Pattern Recognition, 2018

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank.

Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

RankIQA: Learning from Rankings for No-Reference Image Quality Assessment.

Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov

Proceedings of the IEEE International Conference on Computer Vision, 2017

2015

Suitability of Real-Time Image under Complicated Environment Based on Contourlet in SMN.

Lu Yu, Yongmei Cheng, Xialei Liu, Nan Liu

Proceedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering, 2015