24 citations · 43 across the 9 of their papers we have counts for
9 papers
Memory-Efficient Fine-Tuning via Low-Rank Activation Compression
Jiang-Xin Shi, Wen-Da Wei, Jin-Fei Qi +3
The parameter-efficient fine-tuning paradigm has garnered significant attention with the advancement of foundation models. Although numerous methods have been proposed to reduce th…
Unlabeled Data vs. Pre-trained Knowledge: Rethinking SSL in the Era of Large Models
Song-Lin Lv, Rui Zhu, Tong Wei +2
Semi-supervised learning (SSL) alleviates the cost of data labeling process by exploiting unlabeled data and has achieved promising results. Meanwhile, with the development of larg…
LIFT+: Lightweight Fine-Tuning for Long-Tail Learning
Jiang-Xin Shi, Tong Wei, Yu-Feng Li
The fine-tuning paradigm has emerged as a prominent approach for addressing long-tail learning tasks in the era of foundation models. However, the impact of fine-tuning strategies…
Vision-Language Models are Strong Noisy Label Detectors
Tong Wei, Hao-Tian Li, Chun-Shu Li +3
Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled da…
Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model
Jiang-Xin Shi, Chi Zhang, Tong Wei +1
Pre-trained vision-language models like CLIP have shown powerful zero-shot inference ability via image-text matching and prove to be strong few-shot learners in various downstream…
How Re-sampling Helps for Long-Tail Learning?
Jiang-Xin Shi, Tong Wei, Yuke Xiang +1
Long-tail learning has received significant attention in recent years due to the challenge it poses with extremely imbalanced datasets. In these datasets, only a few classes (known…
Robust Long-Tailed Learning under Label Noise
Tong Wei, Jiang-Xin Shi, Wei-Wei Tu +1
Long-tailed learning has attracted much attention recently, with the goal of improving generalisation for tail classes. Most existing works use supervised learning without consider…
NGC: A Unified Framework for Learning with Open-World Noisy Data
Zhi-Fan Wu, Tong Wei, Jianwen Jiang +3
The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance. There…
MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning
Tong Wei, Feng Shi, Hai Wang +1
Learning from positive and unlabeled data (PU learning) is prevalent in practical applications where only a couple of examples are positively labeled. Previous PU learning studies…