10 citations · 19 across the 7 of their papers we have counts for
7 papers
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
Zhuo Huang, Chang Liu, Yinpeng Dong +3
Although vision models such as Contrastive Language-Image Pre-Training (CLIP) show impressive generalization performance, their zero-shot robustness is still limited under Out-of-D…
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning
Zhuo Huang, Li Shen, Jun Yu +2
Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. However, most SSL methods are commonly based on ins…
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization
Zhuo Huang, Muyang Li, Li Shen +4
Out-of-Distribution (OOD) Generalization aims to learn robust models that generalize well to various environments without fitting to distribution-specific features. Recent studies…
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization
Zhuo Huang, Miaoxi Zhu, Xiaobo Xia +6
Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common…
LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers
Zhuoxu Huang, Zhiyou Zhao, Banghuai Li +1
Transformer with its underlying attention mechanism and the ability to capture long-range dependencies makes it become a natural choice for unordered point cloud data. However, sep…
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
Zhuo Huang, Xiaobo Xia, Li Shen +4
Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of dif…
They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning
Zhuo Huang, Ying Tai, Chengjie Wang +2
Semi-Supervised Learning (SSL) with mismatched classes deals with the problem that the classes-of-interests in the limited labeled data is only a subset of the classes in massive u…