3 citations · 4 across the 4 of their papers we have counts for
7 papers
Sparse MLLM Anchors, Dense Adaptation: Breaking the Self-Referential Loop in Wild Test-Time Adaptation
Zhenbin Wang, Lei Zhang, Lituan Wang +3
Wild test-time adaptation (WTTA) updates a source model online under small test batches, concurrent distribution shifts, and time-varying class imbalance. Most WTTA methods derive…
StackTok: Accelerating VLMs Inference with Budget-Adaptive Visual Token Selection
Zhenbin Wang, Lei Zhang, Lituan Wang +3
Increasing image resolution produces ever-longer visual-token sequences in vision-language models (VLMs), substantially raising their inference cost. To reduce this overhead withou…
Layers, Sinks, and Scaling: Adaptive Evidence Selection for Multimodal Large Language Models
Zhenbin Wang, Lei Zhang, Lituan Wang +3
Multimodal large language models (MLLMs) can answer knowledge-intensive visual questions by combining visual evidence from images with facts retrieved from external sources. Howeve…
EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation
Wang Lituan, Zhang Lei, Wang Yan +3
Weakly-supervised medical image segmentation is gaining traction as it requires only rough annotations rather than accurate pixel-to-pixel labels, thereby reducing the workload for…
Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation
Zhenbin Wang, Lei Zhang, Lituan Wang +2
Medical video generation models are expected to have a profound impact on the healthcare industry, including but not limited to medical education and training, surgical planning, a…
Soft Masked Mamba Diffusion Model for CT to MRI Conversion
Zhenbin Wang, Lei Zhang, Lituan Wang +1
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the predominant modalities utilized in the field of medical imaging. Although MRI capture the complexity of anatom…
LanDA: Language-Guided Multi-Source Domain Adaptation
Zhenbin Wang, Lei Zhang, Lituan Wang +1
Multi-Source Domain Adaptation (MSDA) aims to mitigate changes in data distribution when transferring knowledge from multiple labeled source domains to an unlabeled target domain.…