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20192021
most citedShifted Chunk Transformer for Spatio-Temporal Representational Learning

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

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8 papers · 1 filter

cs.CV2021★ 7 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-…

cs.CV2021★ 4 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.CV2020★ 3 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.CV2020★ 7 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.CV2020★ 5 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…

cs.CV2020★ 6 cited

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Wentao Zhu, Can Zhao, Wenqi Li +3

Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data paralle…