24 citations · 67 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
Prototypical Classifier for Robust Class-Imbalanced Learning
Tong Wei, Jiang-Xin Shi, Yu-Feng Li +1
Deep neural networks have been shown to be very powerful methods for many supervised learning tasks. However, they can also easily overfit to training set biases, i.e., label noise…