8 citations · 15 across the 8 of their papers we have counts for
8 papers
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…
Offline Imitation Learning with Model-based Reverse Augmentation
Jie-Jing Shao, Hao-Sen Shi, Lan-Zhe Guo +1
In offline Imitation Learning (IL), one of the main challenges is the \textit{covariate shift} between the expert observations and the actual distribution encountered by the agent,…
DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
Zhi Zhou, Ming Yang, Jiang-Xin Shi +2
Vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot capabilities for various downstream tasks. Their performance can be further enhanced through few…
Interactive Reweighting for Mitigating Label Quality Issues
Weikai Yang, Yukai Guo, Jing Wu +4
Label quality issues, such as noisy labels and imbalanced class distributions, have negative effects on model performance. Automatic reweighting methods identify problematic sample…
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…
Investigating the Limitation of CLIP Models: The Worst-Performing Categories
Jie-Jing Shao, Jiang-Xin Shi, Xiao-Wen Yang +2
Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks.…
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
Jiang-Xin Shi, Tong Wei, Zhi Zhou +3
The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts perf…