10 citations · 25 across the 14 of their papers we have counts for
11 papers · 1 filter
SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images
Yicheng Song, Tiancheng Lin, Die Peng +2
Various multi-instance learning (MIL) based approaches have been developed and successfully applied to whole-slide pathological images (WSI). Existing MIL methods emphasize the imp…
Relational Contrastive Learning and Masked Image Modeling for Scene Text Recognition
Tiancheng Lin, Jinglei Zhang, Yi Xu +3
Context-aware methods have achieved remarkable advancements in supervised scene text recognition by leveraging semantic priors from words. Considering the heterogeneity of text and…
Background Clustering Pre-training for Few-shot Segmentation
Zhimiao Yu, Tiancheng Lin, Yi Xu
Recent few-shot segmentation (FSS) methods introduce an extra pre-training stage before meta-training to obtain a stronger backbone, which has become a standard step in few-shot le…
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…
Solving The Long-Tailed Problem via Intra- and Inter-Category Balance
Renhui Zhang, Tiancheng Lin, Rui Zhang +1
Benchmark datasets for visual recognition assume that data is uniformly distributed, while real-world datasets obey long-tailed distribution. Current approaches handle the long-tai…
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction
Tiancheng Lin, Hongteng Xu, Canqian Yang +1
When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also…
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…
Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection
Tiancheng Lin, Yuanfan Guo, Canqian Yang +2
Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients. Due to lack of public dataset and expert-level annotations, automatic detection o…