288 citations · 301 across the 11 of their papers we have counts for
10 papers · 1 filter
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels
Erjian Guo, Zhen Zhao, Zicheng Wang +3
Medical Visual Question Answering (Med-VQA) systems benefit the interpretation of medical images containing critical clinical information. However, the challenge of noisy labels an…
Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels
Erjian Guo, Zicheng Wang, Zhen Zhao +1
Accurate medical image segmentation is often hindered by noisy labels in training data, due to the challenges of annotating medical images. Prior research works addressing noisy la…
UniBrain: A Unified Model for Cross-Subject Brain Decoding
Zicheng Wang, Zhen Zhao, Luping Zhou +1
Brain decoding aims to reconstruct original stimuli from fMRI signals, providing insights into interpreting mental content. Current approaches rely heavily on subject-specific mode…
MoTe: Learning Motion-Text Diffusion Model for Multiple Generation Tasks
Yiming Wu, Wei Ji, Kecheng Zheng +2
Recently, human motion analysis has experienced great improvement due to inspiring generative models such as the denoising diffusion model and large language model. While the exist…
Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach
Qihe Pan, Zhen Zhao, Zicheng Wang +5
A plethora of text-guided image editing methods has recently been developed by leveraging the impressive capabilities of large-scale diffusion-based generative models especially St…
PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis
Zicheng Wang, Zhenghao Chen, Yiming Wu +3
Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, the…
SOEDiff: Efficient Distillation for Small Object Editing
Yiming Wu, Qihe Pan, Zhen Zhao +3
In this paper, we delve into a new task known as small object editing (SOE), which focuses on text-based image inpainting within a constrained, small-sized area. Despite the remark…
Clean Label Disentangling for Medical Image Segmentation with Noisy Labels
Zicheng Wang, Zhen Zhao, Erjian Guo +1
Current methods focusing on medical image segmentation suffer from incorrect annotations, which is known as the noisy label issue. Most medical image segmentation with noisy labels…
Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
Zhen Zhao, Zicheng Wang, Longyue Wang +3
Semi-supervised medical image segmentation studies have shown promise in training models with limited labeled data. However, current dominant teacher-student based approaches can s…
Progressive Classifier and Feature Extractor Adaptation for Unsupervised Domain Adaptation on Point Clouds
Zicheng Wang, Zhen Zhao, Yiming Wu +2
Unsupervised domain adaptation (UDA) is a critical challenge in the field of point cloud analysis. Previous works tackle the problem either by feature extractor adaptation to enabl…