1 citations · 2 across the 7 of their papers we have counts for
7 papers
Explicit Uncertainty Modeling for Active CLIP Adaptation with Dual Prompt Tuning
Qian-Wei Wang, Yaguang Song, Shu-Tao Xia
Pre-trained vision-language models such as CLIP exhibit strong transferability, yet adapting them to downstream image classification tasks under limited annotation budgets remains…
Fine-tuning Pre-trained Vision-Language Models in a Human-Annotation-Free Manner
Qian-Wei Wang, Guanghao Meng, Ren Cai +2
Large-scale vision-language models (VLMs) such as CLIP exhibit strong zero-shot generalization, but adapting them to downstream tasks typically requires costly labeled data. Existi…
Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation
Qian-Wei Wang, Yaguang Song, Shu-Tao Xia
In the context of noisy partial label learning (NPLL), each training sample is associated with a set of candidate labels annotated by multiple noisy annotators. With the emergence…
Pre-Trained Vision-Language Models as Partial Annotators
Qian-Wei Wang, Yuqiu Xie, Letian Zhang +2
Pre-trained vision-language models learn massive data to model unified representations of images and natural languages, which can be widely applied to downstream machine learning t…
Delving into Identify-Emphasize Paradigm for Combating Unknown Bias
Bowen Zhao, Chen Chen, Qian-Wei Wang +2
Dataset biases are notoriously detrimental to model robustness and generalization. The identify-emphasize paradigm appears to be effective in dealing with unknown biases. However,…
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data
Qian-Wei Wang, Bowen Zhao, Mingyan Zhu +3
Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid. In recent years, benefiting from the stron…
Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignment
Bowen Zhao, Chen Chen, Qian-Wei Wang +2
Models notoriously suffer from dataset biases which are detrimental to robustness and generalization. The identify-emphasize paradigm shows a promising effect in dealing with unkno…