Xiaobo Xia

Orcid: 0000-0003-3615-0919

According to our database¹, Xiaobo Xia authored at least 48 papers between 2019 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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On csauthors.net:

Bibliography

2024

Regularly Truncated M-Estimators for Learning With Noisy Labels.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

IEEE Trans. Multim., 2024

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

[BibT_eX]

[DOI]

CoRR, 2024

Tackling Noisy Labels with Network Parameter Additive Decomposition.

[BibT_eX]

[DOI]

CoRR, 2024

Mitigating Label Noise on Graph via Topological Sample Selection.

[BibT_eX]

[DOI]

CoRR, 2024

Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Dynamics-aware loss for learning with label noise.

[BibT_eX]

[DOI]

Pattern Recognit., December, 2023

Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels.

[BibT_eX]

[DOI]

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

One Shot Learning as Instruction Data Prospector for Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance.

[BibT_eX]

[DOI]

CoRR, 2023

Coreset Selection with Prioritized Multiple Objectives.

[BibT_eX]

[DOI]

CoRR, 2023

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources.

[BibT_eX]

[DOI]

CoRR, 2023

IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Regularly Truncated M-estimators for Learning with Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2023

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision.

[BibT_eX]

[DOI]

CoRR, 2023

Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds.

[BibT_eX]

[DOI]

CoRR, 2023

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Holistic Label Correction for Noisy Multi-Label Classification.

[BibT_eX]

[DOI]

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

Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples.

[BibT_eX]

[DOI]

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

HumanMAC: Masked Motion Completion for Human Motion Prediction.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Vis. Informatics, 2022

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

[BibT_eX]

[DOI]

IEEE Trans. Multim., 2022

Pluralistic Image Completion with Probabilistic Mixture-of-Experts.

[BibT_eX]

[DOI]

CoRR, 2022

Pluralistic Image Completion with Gaussian Mixture Models.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Selective-Supervised Contrastive Learning with Noisy Labels.

[BibT_eX]

[DOI]

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

2021

Learning lightweight super-resolution networks with weight pruning.

[BibT_eX]

[DOI]

Neural Networks, 2021

Kernel Mean Estimation by Marginalized Corrupted Distributions.

[BibT_eX]

[DOI]

CoRR, 2021

Instance Correction for Learning with Open-set Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

Comput. Methods Programs Biomed., 2021

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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

2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Parts-dependent Label Noise: Towards Instance-dependent Label Noise.

[BibT_eX]

[DOI]

CoRR, 2020

Class2Simi: A New Perspective on Learning with Label Noise.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Part-dependent Label Noise: Towards Instance-dependent Label Noise.

[BibT_eX]

[DOI]

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?

[BibT_eX]

[DOI]

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

Xiaobo Xia

Timeline

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On csauthors.net:

Bibliography

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