no code implementations • 11 May 2024 • Jinkun Jiang, Qingxuan Lv, Yuezun Li, Yong Du, Sheng Chen, Hui Yu, Junyu Dong
The drawback of these methods includes: 1) the pair-wise relation is limited to exposing the underlying correlations of two more samples, hindering the exploration of the structural information embedded in the target domain; 2) the clustering process only relies on the semantic feature, while overlooking the critical effect of domain shift, i. e., the distribution differences between the source and target domains.
no code implementations • 8 Mar 2024 • Junyu Chen, Yihao Liu, Shuwen Wei, Zhangxing Bian, Aaron Carass, Yong Du
Here, we propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration.
no code implementations • 22 Oct 2023 • Yong Du, Jiahui Zhan, Shengfeng He, Xinzhe Li, Junyu Dong, Sheng Chen, Ming-Hsuan Yang
In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences.
no code implementations • 28 Jul 2023 • Junyu Chen, Yihao Liu, Shuwen Wei, Zhangxing Bian, Shalini Subramanian, Aaron Carass, Jerry L. Prince, Yong Du
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade.
1 code implementation • CVPR 2023 • Yu Zheng, Jiahui Zhan, Shengfeng He, Junyu Dong, Yong Du
In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one.
Ranked #2 on Image Dehazing on SOTS Indoor
no code implementations • 10 Mar 2023 • Junyu Chen, Yihao Liu, Yufan He, Yong Du
Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration.
no code implementations • 10 Mar 2023 • Junyu Chen, Yihao Liu, Yufan He, Yong Du
In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions.
1 code implementation • 17 Jul 2022 • Haorui Song, Yong Du, Tianyi Xiang, Junyu Dong, Jing Qin, Shengfeng He
Consequently, in the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction.
1 code implementation • 19 Nov 2021 • Junyu Chen, Eric C. Frey, Yufan He, William P. Segars, Ye Li, Yong Du
Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications.
Ranked #1 on Medical Image Registration on OASIS
2 code implementations • ICCV 2021 • Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, Shengfeng He
This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted code is semantically accessible from one of the other and fastened in a editable domain; 2) enforcing inter-image coherence, such that the fidelity of each inverted code can be maximized with the complement of other images.
no code implementations • CVPR 2021 • Sucheng Ren, Yong Du, Jianming Lv, Guoqiang Han, Shengfeng He
To these ends, we introduce a trainable "master" network which ingests both audio signals and silent lip videos instead of a pretrained teacher.
1 code implementation • 19 Apr 2021 • Shengfeng He, Bing Peng, Junyu Dong, Yong Du
Shadow removal is an important yet challenging task in image processing and computer vision.
1 code implementation • 17 Apr 2021 • Junyu Chen, Ye Li, Licia P. Luna, Hyun Woo Chung, Steven P. Rowe, Yong Du, Lilja B. Solnes, Eric C. Frey
The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT.
1 code implementation • 13 Apr 2021 • Junyu Chen, Yufan He, Eric C. Frey, Ye Li, Yong Du
However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image.
Ranked #4 on Medical Image Registration on OASIS
no code implementations • 22 May 2020 • TAIZHONG YE, Yong Du, JUNJIE DENG, AND SHENGFENG HE
In this paper, we propose to embed color information into an invertible grayscale, such that it can be easily recovered to the original color.
1 code implementation • 6 Dec 2019 • Junyu Chen, Ye Li, Yong Du, Eric C. Frey
In this work, we present a novel image registration method for creating highly anatomically detailed anthropomorphic phantoms from a single digital phantom.
no code implementations • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 • Yong Du, Wei Wang, Liang Wang
Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers.
Ranked #118 on Skeleton Based Action Recognition on NTU RGB+D