Papers by Xianhui Cao
LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained vision-language models (VLMs) often suffer from bias in class-imbalanced scenes. |
| Approach: | They propose a multi-dimensional dynamic prompt routing framework that integrates a knowledge base for classes spanning multiple visual-semantic dimensions. |
| Outcome: | The proposed framework achieves comparable results with current SOTA methods on long-tailed benchmarks, including CIFAR-LT, ImageNet-LT and Places-LT. |