5 citations · 11 across the 8 of their papers we have counts for
5 papers · 1 filter
Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models
Shuoyuan Wang, Yixuan Li, Hongxin Wei
Confidence calibration is critical for the safe deployment of machine learning models in the real world. However, such issue in vision-language models like CLIP, particularly after…
Impact of Noisy Supervision in Foundation Model Learning
Hao Chen, Zihan Wang, Ran Tao +5
Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning. However, the large-scale pre-training datasets, often inacces…
Open-Vocabulary Calibration for Fine-tuned CLIP
Shuoyuan Wang, Jindong Wang, Guoqing Wang +3
Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual c…
Conformal Prediction for Deep Classifier via Label Ranking
Jianguo Huang, Huajun Xi, Linjun Zhang +3
Conformal prediction is a statistical framework that generates prediction sets containing ground-truth labels with a desired coverage guarantee. The predicted probabilities produce…
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
Hao Chen, Jindong Wang, Ankit Shah +5
Pre-training on large-scale datasets and then fine-tuning on downstream tasks have become a standard practice in deep learning. However, pre-training data often contain label noise…