25 citations · 49 across the 6 of their papers we have counts for
8 papers
Reinforcement Learning with Visually Attributable On-Policy Self-Distillation
Shiao Xie, Siyu Chen, Jianwei Lv +2
Multimodal reinforcement learning with verifiable rewards (RLVR) provides reliable task-level supervision, yet offers limited guidance for localizing errors in visual reasoning. On…
EGSD: Event-Grounded Self-Distillation for Streaming Video Understanding
Yuwei Miao, Xuesheng Zhang, Wenhao Zou +5
Real-time video understanding requires incrementally maintaining a memory of streaming content, and optimizing this requires dense process signals. On-Policy Self-Distillation (OPS…
G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation
Shiao Xie, Siyu Chen, Jianwei Lv +3
Personalized interpretation of medical reports has emerged as an increasingly important need among patients. Addressing this need requires both evidence-grounded medical factuality…
SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation
Shiao Xie, Hongyi Wang, Ziwei Niu +4
Semi-supervised learning (SSL) for medical image segmentation is a challenging yet highly practical task, which reduces reliance on large-scale labeled dataset by leveraging unlabe…
HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification
Shuyi Ouyang, Hongyi Wang, Ziwei Niu +6
The task of multi-label image classification involves recognizing multiple objects within a single image. Considering both valuable semantic information contained in the labels and…
A Survey on Domain Generalization for Medical Image Analysis
Ziwei Niu, Shuyi Ouyang, Shiao Xie +2
Medical Image Analysis (MedIA) has emerged as a crucial tool in computer-aided diagnosis systems, particularly with the advancement of deep learning (DL) in recent years. However,…
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise Perspective for Medical Image Segmentation
Huimin Huang, Shiao Xie1, Lanfen Lin +4
Recently, a variety of vision transformers have been developed as their capability of modeling long-range dependency. In current transformer-based backbones for medical image segme…
Mixed Transformer U-Net For Medical Image Segmentation
Hongyi Wang, Shiao Xie, Lanfen Lin +4
Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers h…