activity
20192021
most citedShifted Chunk Transformer for Spatio-Temporal Representational Learning

7 citations · 37 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV20217 cited

Shifted Chunk Transformer for Spatio-Temporal Representational Learning

Xuefan Zha, Wentao Zhu, Tingxun Lv +2

Spatio-temporal representational learning has been widely adopted in various fields such as action recognition, video object segmentation, and action anticipation. Previous spatio-…

eess.IV20211 cited

Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures

Holger R. Roth, Dong Yang, Wenqi Li +5

Building robust deep learning-based models requires diverse training data, ideally from several sources. However, these datasets cannot be combined easily because of patient privac…

cs.CV20214 cited

Test-Time Training for Deformable Multi-Scale Image Registration

Wentao Zhu, Yufang Huang, Daguang Xu +3

Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentatio…

cs.CV20203 cited

Deformable Gabor Feature Networks for Biomedical Image Classification

Xuan Gong, Xin Xia, Wentao Zhu +3

In recent years, deep learning has dominated progress in the field of medical image analysis. We find however, that the ability of current deep learning approaches to represent the…

cs.CV20207 cited

Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer

Yufang Huang, Wentao Zhu, Deyi Xiong +3

Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation. In this paper, we propose a novel neural approach…

cs.CV20205 cited

Multi-Domain Image Completion for Random Missing Input Data

Liyue Shen, Wentao Zhu, Xiaosong Wang +9

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-par…