M
Meng-Hao Guo
Researcher at Tsinghua University
Publications - 11
Citations - 1400
Meng-Hao Guo is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 6, co-authored 11 publications receiving 244 citations. Previous affiliations of Meng-Hao Guo include Xidian University.
Papers
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Journal ArticleDOI
PCT: Point cloud transformer
TL;DR: A novel framework based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing, is presented, which is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.
Journal ArticleDOI
PCT: Point cloud transformer
TL;DR: Point Cloud Transformer (PCT) as mentioned in this paper is based on Transformer, which is inherently permutation invariant for processing a sequence of points, making it well suited for point cloud learning.
Posted Content
Attention Mechanisms in Computer Vision: A Survey.
Meng-Hao Guo,Tian-Xing Xu,Jiangjiang Liu,Zheng-Ning Liu,Peng-Tao Jiang,Tai-Jiang Mu,Song-Hai Zhang,Ralph R. Martin,Ming-Ming Cheng,Shi-Min Hu +9 more
TL;DR: A comprehensive review of attention mechanisms in computer vision can be found in this article, which categorizes them according to approach, such as channel attention, spatial attention, temporal attention and branch attention.
Book ChapterDOI
Semi-supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model
Wenhui Cui,Yanlin Liu,Yuxing Li,Meng-Hao Guo,Yiming Li,Xiuli Li,Tianle Wang,Xiangzhu Zeng,Chuyang Ye +8 more
TL;DR: In this paper, a semi-supervised learning (SSL) approach was proposed for brain lesion segmentation, where unannotated data was incorporated into the training of CNNs and a loss of segmentation consistency was designed and integrated into a self-ensembling framework.
Posted Content
Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model
Wenhui Cui,Yanlin Liu,Yuxing Li,Meng-Hao Guo,Yiming Li,Xiuli Li,Tianle Wang,Xiangzhu Zeng,Chuyang Ye +8 more
TL;DR: This work proposes a semi-supervised learning (SSL) approach to brain lesion segmentation, where unannotated data is incorporated into the training of CNNs and outperforms competing SSL-based methods on ischemic stroke lesion segmentsation.