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Xiaobo Xia
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2020 – today
- 2024
- [j8]Xiaobo Xia
, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu:
Regularly Truncated M-Estimators for Learning With Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3522-3536 (2024) - [j7]Zhengning Wu
, Tianyu He
, Xiaobo Xia
, Jun Yu
, Xu Shen, Tongliang Liu
:
Conditional Consistency Regularization for Semi-Supervised Multi-Label Image Classification. IEEE Trans. Multim. 26: 4206-4216 (2024) - [c20]Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen, Jiale Liu, Qingyun Wu, Tongliang Liu:
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models. ICLR 2024 - [i31]Zhaoqing Wang, Xiaobo Xia, Ziye Chen, Xiao He, Yandong Guo, Mingming Gong, Tongliang Liu:
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision. CoRR abs/2402.08960 (2024) - [i30]Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu:
Mitigating Label Noise on Graph via Topological Sample Selection. CoRR abs/2403.01942 (2024) - [i29]Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu:
Tackling Noisy Labels with Network Parameter Additive Decomposition. CoRR abs/2403.13241 (2024) - [i28]Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu:
Few-Shot Adversarial Prompt Learning on Vision-Language Models. CoRR abs/2403.14774 (2024) - [i27]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:
DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception. CoRR abs/2405.15232 (2024) - [i26]Lei Zhang, Yunshui Li, Jiaming Li, Xiaobo Xia, Jiaxi Yang, Run Luo, Minzheng Wang, Longze Chen, Junhao Liu, Min Yang:
Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs. CoRR abs/2406.18294 (2024) - 2023
- [j6]Xiaobo Xia
, Bo Han
, Nannan Wang
, Jiankang Deng
, Jiatong Li, Yinian Mao, Tongliang Liu
:
Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 3047-3058 (2023) - [j5]Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu
, Xu-Yao Zhang, Cheng-Lin Liu
:
Dynamics-aware loss for learning with label noise. Pattern Recognit. 144: 109835 (2023) - [c19]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CVPR 2023: 16175-16185 - [c18]Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu:
Holistic Label Correction for Noisy Multi-Label Classification. ICCV 2023: 1483-1493 - [c17]Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu:
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples. ICCV 2023: 1833-1843 - [c16]Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu:
HumanMAC: Masked Motion Completion for Human Motion Prediction. ICCV 2023: 9510-9521 - [c15]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. ICLR 2023 - [c14]Yong Lin, Renjie Pi, Weizhong Zhang, Xiaobo Xia, Jiahui Gao, Xiao Zhou, Tongliang Liu, Bo Han:
A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond. ICLR 2023 - [c13]Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu:
Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning. ICLR 2023 - [c12]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. NeurIPS 2023 - [i25]Ling-Hao Chen
, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu
:
HumanMAC: Masked Motion Completion for Human Motion Prediction. CoRR abs/2302.03665 (2023) - [i24]Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu:
Dynamics-Aware Loss for Learning with Label Noise. CoRR abs/2303.11562 (2023) - [i23]Zhuo Huang, Miaoxi Zhu, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, Bo Du, Tongliang Liu:
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization. CoRR abs/2303.13087 (2023) - [i22]Shikun Li, Xiaobo Xia, Jiankang Deng, Shiming Ge, Tongliang Liu:
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds. CoRR abs/2306.03116 (2023) - [i21]Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu:
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR abs/2306.07036 (2023) - [i20]Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu:
Regularly Truncated M-estimators for Learning with Noisy Labels. CoRR abs/2309.00894 (2023) - [i19]Shikun Li, Xiaobo Xia, Hansong Zhang, Shiming Ge, Tongliang Liu:
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm. CoRR abs/2309.12706 (2023) - [i18]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
:
VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence. CoRR abs/2310.04992 (2023) - [i17]Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen, Jiale Liu, Qingyun Wu, Tongliang Liu:
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models. CoRR abs/2310.10873 (2023) - [i16]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. CoRR abs/2311.03236 (2023) - [i15]Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Tongliang Liu:
Coreset Selection with Prioritized Multiple Objectives. CoRR abs/2311.08675 (2023) - [i14]Linghao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, Tongliang Liu:
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance. CoRR abs/2312.08852 (2023) - [i13]Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li:
One Shot Learning as Instruction Data Prospector for Large Language Models. CoRR abs/2312.10302 (2023) - 2022
- [j4]Zhengning Wu
, Xiaobo Xia
, Ruxin Wang
, Jiatong Li, Jun Yu
, Yinian Mao, Tongliang Liu
:
LR-SVM+: Learning Using Privileged Information with Noisy Labels. IEEE Trans. Multim. 24: 1080-1092 (2022) - [j3]Shijun Cai, Seok-Hee Hong, Xiaobo Xia, Tongliang Liu
, Weidong Huang
:
A machine learning approach for predicting human shortest path task performance. Vis. Informatics 6(2): 50-61 (2022) - [c11]Shikun Li
, Xiaobo Xia, Shiming Ge, Tongliang Liu
:
Selective-Supervised Contrastive Learning with Noisy Labels. CVPR 2022: 316-325 - [c10]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR 2022 - [c9]Shuo Yang, Peize Sun, Yi Jiang, Xiaobo Xia, Ruiheng Zhang, Zehuan Yuan, Changhu Wang, Ping Luo, Min Xu:
Objects in Semantic Topology. ICLR 2022 - [c8]Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu
:
Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data. KDD 2022: 2110-2119 - [c7]Yewen Li, Chaojie Wang, Xiaobo Xia, Tongliang Liu, Xin Miao, Bo An:
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE. NeurIPS 2022 - [c6]Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu:
Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning. NeurIPS 2022 - [c5]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Gaussian Mixture Models. NeurIPS 2022 - [i12]Shikun Li
, Xiaobo Xia, Shiming Ge, Tongliang Liu:
Selective-Supervised Contrastive Learning with Noisy Labels. CoRR abs/2203.04181 (2022) - [i11]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu
:
Pluralistic Image Completion with Probabilistic Mixture-of-Experts. CoRR abs/2205.09086 (2022) - [i10]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu
:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. CoRR abs/2207.03162 (2022) - 2021
- [j2]Fei Long, Jing-Jie Peng, Weitao Song, Xiaobo Xia, Jun Sang
:
BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells. Comput. Methods Programs Biomed. 202: 105972 (2021) - [j1]Xinrui Jiang, Nannan Wang
, Jingwei Xin, Xiaobo Xia
, Xi Yang, Xinbo Gao
:
Learning lightweight super-resolution networks with weight pruning. Neural Networks 144: 21-32 (2021) - [c4]Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang:
Robust early-learning: Hindering the memorization of noisy labels. ICLR 2021 - [c3]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. ICML 2021: 11285-11295 - [i9]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR abs/2106.00445 (2021) - [i8]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Instance Correction for Learning with Open-set Noisy Labels. CoRR abs/2106.00455 (2021) - [i7]Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu:
Kernel Mean Estimation by Marginalized Corrupted Distributions. CoRR abs/2107.04855 (2021) - [i6]Shuo Yang, Peize Sun, Yi Jiang, Xiaobo Xia, Ruiheng Zhang, Zehuan Yuan, Changhu Wang, Ping Luo, Min Xu:
Objects in Semantic Topology. CoRR abs/2110.02687 (2021) - 2020
- [c2]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Part-dependent Label Noise: Towards Instance-dependent Label Noise. NeurIPS 2020 - [i5]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Multi-Class Classification from Noisy-Similarity-Labeled Data. CoRR abs/2002.06508 (2020) - [i4]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A New Perspective on Learning with Label Noise. CoRR abs/2006.07831 (2020) - [i3]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Parts-dependent Label Noise: Towards Instance-dependent Label Noise. CoRR abs/2006.07836 (2020) - [i2]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao:
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels. CoRR abs/2012.00932 (2020)
2010 – 2019
- 2019
- [c1]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? NeurIPS 2019: 6835-6846 - [i1]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? CoRR abs/1906.00189 (2019)
Coauthor Index
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last updated on 2024-08-08 19:18 CEST by the dblp team
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