8 citations · 10 across the 5 of their papers we have counts for
5 papers
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment
Hongyu Hu, Tiancheng Lin, Jie Wang +2
Large-scale vision-language models (VLMs), e.g., CLIP, learn broad visual concepts from tedious training data, showing superb generalization ability. Amount of prompt learning meth…
Relational Contrastive Learning for Scene Text Recognition
Jinglei Zhang, Tiancheng Lin, Yi Xu +2
Context-aware methods achieved great success in supervised scene text recognition via incorporating semantic priors from words. We argue that such prior contextual information can…
SLPD: Slide-level Prototypical Distillation for WSIs
Zhimiao Yu, Tiancheng Lin, Yi Xu
Improving the feature representation ability is the foundation of many whole slide pathological image (WSIs) tasks. Recent works have achieved great success in pathological-specifi…
Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images
Tiancheng Lin, Zhimiao Yu, Hongyu Hu +2
Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailin…
Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning
Shengjia Zhang, Tiancheng Lin, Yi Xu
By leveraging data from a fully labeled source domain, unsupervised domain adaptation (UDA) improves classification performance on an unlabeled target domain through explicit discr…