1 citations · 2 across the 7 of their papers we have counts for
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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…