most cited3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

38 citations · 79 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2024

FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching

Sucheng Ren, Qihang Yu, Ju He +3

Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through…

cs.CV20244 cited

An Image is Worth 32 Tokens for Reconstruction and Generation

Qihang Yu, Mark Weber, Xueqing Deng +3

Recent advancements in generative models have highlighted the crucial role of image tokenization in the efficient synthesis of high-resolution images. Tokenization, which transform…

cs.CV2024

COCONut: Modernizing COCO Segmentation

Xueqing Deng, Qihang Yu, Peng Wang +2

In recent decades, the vision community has witnessed remarkable progress in visual recognition, partially owing to advancements in dataset benchmarks. Notably, the established COC…

cs.CV20241 cited

ViTamin: Designing Scalable Vision Models in the Vision-Language Era

Jieneng Chen, Qihang Yu, Xiaohui Shen +2

Recent breakthroughs in vision-language models (VLMs) start a new page in the vision community. The VLMs provide stronger and more generalizable feature embeddings compared to thos…

cs.CV20231 cited

Towards Open-Ended Visual Recognition with Large Language Model

Qihang Yu, Xiaohui Shen, Liang-Chieh Chen

Localizing and recognizing objects in the open-ended physical world poses a long-standing challenge within the domain of machine perception. Recent methods have endeavored to addre…

cs.CV202338 cited

3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

Jieneng Chen, Jieru Mei, Xianhang Li +12

Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, h…