Xiaobo Xia

According to our database, Xiaobo Xia authored at least 54 papers between 2019 and 2024.

Collaborative distances:

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

2024

Transferring Annotator- and Instance-Dependent Transition Matrix for Learning From Crowds.

Shikun Li, Xiaobo Xia, Jiankang Deng, Shiming Ge, Tongliang Liu

IEEE Trans. Pattern Anal. Mach. Intell., November, 2024

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 Trans. Pattern Anal. Mach. Intell., September, 2024

Regularly Truncated M-Estimators for Learning With Noisy Labels.

Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu

IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

Conditional Consistency Regularization for Semi-Supervised Multi-Label Image Classification.

Zhengning Wu, Tianyu He, Xiaobo Xia, Jun Yu, Xu Shen, Tongliang Liu

IEEE Trans. Multim., 2024

MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct.

Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Xiaobo Xia, Fei Huang, Jingkuan Song, Yongbin Li

CoRR, 2024

Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection.

Yewen Li, Chaojie Wang, Xiaobo Xia, Xu He, Ruyi An, Dong Li, Tongliang Liu, Bo An, Xinrun Wang

CoRR, 2024

Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs.

Lei Zhang, Yunshui Li, Jiaming Li, Xiaobo Xia, Jiaxi Yang, Run Luo, Minzheng Wang, Longze Chen, Junhao Liu, Min Yang

CoRR, 2024

DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception.

Run Luo, Yunshui Li, Longze Chen, Wanwei He, Ting-En Lin, Ziqiang Liu, Lei Zhang, Zikai Song, Xiaobo Xia, Tongliang Liu, Min Yang, Binyuan Hui

CoRR, 2024

Few-Shot Adversarial Prompt Learning on Vision-Language Models.

Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu

CoRR, 2024

Mitigating Label Noise on Graph via Topological Sample Selection.

Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu

CoRR, 2024

Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision.

Zhaoqing Wang, Xiaobo Xia, Ziye Chen, Xiao He, Yandong Guo, Mingming Gong, Tongliang Liu

CoRR, 2024

Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints.

Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Hongxin Wei, Tongliang Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mitigating Label Noise on Graphs via Topological Sample Selection.

Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Realistic Model Selection for Semi-supervised Learning.

Muyang Li, Xiaobo Xia, Runze Wu, Fengming Huang, Jun Yu, Bo Han, Tongliang Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

ERASE: Error-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

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

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, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023

Dynamics-aware loss for learning with label noise.

Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu

Pattern Recognit., December, 2023

Extended $T$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 Trans. Pattern Anal. Mach. Intell., March, 2023

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

CoRR, 2023

Coreset Selection with Prioritized Multiple Objectives.

Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Tongliang Liu

CoRR, 2023

VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence.

Jianing Qiu, Jian Wu, Hao Wei, Peilun Shi, Minqing Zhang, Yunyun Sun, Lin Li, Hanruo Liu, Hongyi Liu, Simeng Hou, Yuyang Zhao, Xuehui Shi, Junfang Xian, Xiaoxia Qu, Sirui Zhu, Lijie Pan, Xiaoniao Chen, Xiaojia Zhang, Shuai Jiang, Kebing Wang, Chenlong Yang, Mingqiang Chen, Sujie Fan, Jianhua Hu, Aiguo Lv, Hui Miao, Li Guo, Shujun Zhang, Cheng Pei, Xiaojuan Fan, Jianqin Lei, Ting Wei, Junguo Duan, Chun Liu, Xiaobo Xia, Siqi Xiong, Junhong Li, Benny Lo, Yih Chung Tham, Tien Yin Wong, Ningli Wang, Wu Yuan

CoRR, 2023

Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm.

Shikun Li, Xiaobo Xia, Hansong Zhang, Shiming Ge, Tongliang Liu

CoRR, 2023

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

CoRR, 2023

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style.

Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Holistic Label Correction for Noisy Multi-Label Classification.

Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu

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

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

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

HumanMAC: Masked Motion Completion for Human Motion Prediction.

Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu

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

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization.

Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu

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

2022

A machine learning approach for predicting human shortest path task performance.

Shijun Cai, Seok-Hee Hong, Xiaobo Xia, Tongliang Liu, Weidong Huang

Vis. Informatics, 2022

LR-SVM+: Learning Using Privileged Information with Noisy Labels.

Zhengning Wu, Xiaobo Xia, Ruxin Wang, Jiatong Li, Jun Yu, Yinian Mao, Tongliang Liu

IEEE Trans. Multim., 2022

Pluralistic Image Completion with Probabilistic Mixture-of-Experts.

Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu

CoRR, 2022

Pluralistic Image Completion with Gaussian Mixture Models.

Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning.

Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE.

Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, Xin Miao, Bo An

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data.

Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Objects in Semantic Topology.

Shuo Yang, Peize Sun, Yi Jiang, Xiaobo Xia, Ruiheng Zhang, Zehuan Yuan, Changhu Wang, Ping Luo, Min Xu

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Selective-Supervised Contrastive Learning with Noisy Labels.

Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu

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

2021

Learning lightweight super-resolution networks with weight pruning.

Xinrui Jiang, Nannan Wang, Jingwei Xin, Xiaobo Xia, Xi Yang, Xinbo Gao

Neural Networks, 2021

Kernel Mean Estimation by Marginalized Corrupted Distributions.

Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu

CoRR, 2021

Instance Correction for Learning with Open-set Noisy Labels.

Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama

CoRR, 2021

BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells.

Fei Long, Jing-Jie Peng, Weitao Song, Xiaobo Xia, Jun Sang

Comput. Methods Programs Biomed., 2021

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

Proceedings of the 38th International Conference on Machine Learning, 2021

Robust early-learning: Hindering the memorization of noisy labels.

Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels.

Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao

CoRR, 2020

Parts-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

CoRR, 2020

Class2Simi: A New Perspective on Learning with Label Noise.

Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

CoRR, 2020

Multi-Class Classification from Noisy-Similarity-Labeled Data.

Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

CoRR, 2020

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Are Anchor Points Really Indispensable in Label-Noise Learning?

Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019