Publications

Preprints, Conference Papers, Journal Articles

(*) beside authors' names indicates equal contributions.

(✉) beside authors' names indicates the corresponding author.

Preprints

1. Uni-OVSeg: Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision.
   Zhaoqing Wang, Xiaobo Xia, Ziye Chen, Xiao He, Yandong Guo, Mingming Gong, Tongliang Liu.

2. Few-Shot Adversarial Prompt Learning on Vision-Language Models.
   Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu.

3. One Shot Learning as Instruction Data Prospector for Large Language Models.
   Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li.

4. Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision.
   Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu.

5. ERASE: Erorr-Resilient Representation Learning on Graphs for Label Noise Tolerance.
   Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao^✉, Xiaobo Xia^✉, Tongliang Liu.

Conference Papers

1. Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints.
   Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Hongxin Wei, Tongliang Liu.
   In International Conference on Machine Learning (ICML 2024).

2. Towards Realistic Model Selection for Semi-Supervised Learning.
   Muyang Li, Xiaobo Xia, Runze Wu, Fengming Huang, Jun Yu, Bo Han, Tongliang Liu.
   In International Conference on Machine Learning (ICML 2024).

3. Mitigating Label Noise on Graph via Topological Sample Selection.
   Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu.
   In International Conference on Machine Learning (ICML 2024).

4. IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models.
   Shaokun Zhang*, Xiaobo Xia*^✉, Zhaoqing Wang, Ling-hao Chen, Jiale Liu, Qingyun Wu^✉, Tongliang Liu.
   In International Conference on Learning Representations (ICLR 2024).

5. Out-of-Distribution Detection Learning with Unreliable Out-of-Distribution Sources.
   Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han.
   In Advances in Neural Information Processing Systems (NeurIPS 2023).

6. HumanMAC: Masked Motion Completion for Human Motion Prediction.
   Ling-Hao Chen*, Jiawei Zhang*, Yewen Li, Yiren Pang, Xiaobo Xia^✉, Tongliang Liu.
   In IEEE/CVF International Conference on Computer Vision (ICCV 2023).

7. Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples.
   Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu.
   In IEEE/CVF International Conference on Computer Vision (ICCV 2023).

8. Holistic Label Correction for Noisy Multi-Label Classification.
   Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu.
   In IEEE/CVF International Conference on Computer Vision (ICCV 2023).

9. Robust Generalization against Corruptions via Worst-Case Sharpness Minimization.
   Zhuo Huang*, Miaoxi Zhu*, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu.
   In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023).

10. Moderate Coreset: A Universal Method of Data Selection for Real-World Data-Efficient Deep Learning.
    Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu.
    In International Conference on Learning Representations (ICLR 2023).

11. Harnessing Out-of-Distribution Examples via Augmenting Content and Style.
    Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu.
    In International Conference on Learning Representations (ICLR 2023).

12. A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond.
    Yong Lin*, Renjie Pi*, Weizhong Zhang, Xiaobo Xia, Jiahui Gao, Xiao Zhou, Tongliang Liu, Bo Han.
    In International Conference on Learning Representations (ICLR 2023).
    (This paper was selected for spotlight presentation; rate: 8%)
    

13. Pluralistic Image Completion with Gaussian Mixture Models.
    Xiaobo Xia*, Wenhao Yang*, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu.
    In Advances in Neural Information Processing Systems (NeurIPS 2022).

14. Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning.
    Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu.
    In Advances in Neural Information Processing Systems (NeurIPS 2022).
    (This paper was selected for spotlight presentation; rate: 5%)
    

15. Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE.
    Yewen Li*, Chaojie Wang*, Xiaobo Xia, Tongliang Liu, Miao Xu, Bo An.
    In Advances in Neural Information Processing Systems (NeurIPS 2022).

16. Sample-Efficient Kernel Mean Estimation by Marginalized Corrupted Distributions.
    Xiaobo Xia*, Shuo Shan*, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu.
    In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022).

17. Selective-Supervised Contrastive Learning with Noisy Labels.
    Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu.
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022).

18. Sample Selection with Uncertainty of Losses for Learning with Noisy Labels.
    Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama.
    In International Conference on Learning Representations (ICLR 2022).

19. Objects in Semantic Topology.
    Shuo Yang, Peize Sun, Yi Jiang, Xiaobo Xia, Ruiheng Zhang, Zehuan Yuan, Changhu Wang, Ping Luo, Min Xu.
    In International Conference on Learning Representations (ICLR 2022).

20. Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels.
    Songhua Wu*, Xiaobo Xia*, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu.
    In International Conference on Machine Learning (ICML 2021).

21. Robust Early-Learning: Hindering the Memorization of Noisy Labels.
    Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang.
    In International Conference on Learning Representations (ICLR 2021).

22. Part-Dependent Label Noise: Towards Instance-Dependent Label Noise.
    Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama.
    In Advances in Neural Information Processing Systems (NeurIPS 2020).
    (This paper was selected for spotlight presentation; rate: 3%)
    

23. Are Anchor Points Really Indispensable in Label-Noise Learning?
    Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama.
    In Advances in Neural Information Processing Systems (NeurIPS 2019).

Journal Articles

1. Transfering Annotator-and Instance-dependent Transition Matrix for Learning from Crowds.
   Shikun Li, Xiaobo Xia, Jiankang Deng, Shiming Ge, Tongliang Liu.
   IEEE Transactions on Pattern Analysis and Machine Intelligence 2024.

2. Tackling Noisy Labels with Network Parameter Additive Decomposition.
   Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu.
   IEEE Transactions on Pattern Analysis and Machine Intelligence 2024.

3. Regularly Truncated M-estimators for Learning with Noisy Labels.
   Xiaobo Xia*, Pengqian Lu*, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu.
   IEEE Transactions on Pattern Analysis and Machine Intelligence 2024.

4. Extended T: Learning with Mixed Closed-Set and Open-Set Noisy Labels.
   Xiaobo Xia, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao, Tongliang Liu.
   IEEE Transactions on Pattern Analysis and Machine Intelligence 2023.

5. Dynamics-Aware Loss for Learning with Label Noise.
   Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu.
   Pattern Recognition 2023.

6. Conditional Consistency Regularization for Semi-Supervised Multi-Label Image Classification.
   Zhengning Wu, Tianyu He, Xiaobo Xia, Jun Yu, Xu Shen, Tongliang Liu.
   IEEE Transactions on Multimedia 2023.

7. LR-SVM+: Learning Using Privileged Information with Noisy Labels.
   Zhengning Wu, Xiaobo Xia, Ruxin Wang, Jiatong Li, Jun Yu, Yinian Mao, Tongliang Liu.
   IEEE Transactions on Multimedia 2021.

8. Learning Lightweight Super-Resolution Networks with Weight Pruning.
   Xinrui Jiang, Nannan Wang, Jingwei Xin, Xiaobo Xia, Xi Yang, Xinbo Gao.
   Neural Networks 2020.