24 citations · 67 across the 14 of their papers we have counts for
14 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…
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…
LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM
Zhi Zhou, Kun-Yang Yu, Shi-Yu Tian +6
Large language models (LLMs), both proprietary and open-source, have demonstrated remarkable capabilities across various natural language processing tasks. However, they face signi…
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…
LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
Zhi Zhou, Jiang-Xin Shi, Peng-Xiao Song +4
Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks. Nonetheless,…
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…
A Survey on Extreme Multi-label Learning
Tong Wei, Zhen Mao, Jiang-Xin Shi +2
Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good pe…
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
Tong Wei, Qian-Yu Liu, Jiang-Xin Shi +2
Long-Tailed Semi-Supervised Learning (LTSSL) aims to learn from class-imbalanced data where only a few samples are annotated. Existing solutions typically require substantial cost…
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…
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…