1 code implementation • 22 Sep 2022 • Deying Kong, Linguang Zhang, Liangjian Chen, Haoyu Ma, Xiangyi Yan, Shanlin Sun, Xingwei Liu, Kun Han, Xiaohui Xie
In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject.
1 code implementation • 16 Sep 2022 • Haoyu Ma, Zhe Wang, Yifei Chen, Deying Kong, Liangjian Chen, Xingwei Liu, Xiangyi Yan, Hao Tang, Xiaohui Xie
In this paper, we propose the token-Pruned Pose Transformer (PPT) for 2D human pose estimation, which can locate a rough human mask and performs self-attention only within selected tokens.
Ranked #17 on 3D Human Pose Estimation on Human3.6M (using extra training data)
1 code implementation • 18 Oct 2021 • Haoyu Ma, Liangjian Chen, Deying Kong, Zhe Wang, Xingwei Liu, Hao Tang, Xiangyi Yan, Yusheng Xie, Shih-Yao Lin, Xiaohui Xie
The 3D position encoding guided by the epipolar field provides an efficient way of encoding correspondences between pixels of different views.
Ranked #20 on 3D Human Pose Estimation on Human3.6M (using extra training data)
1 code implementation • ICCV 2021 • Hao Tang, Xingwei Liu, Shanlin Sun, Xiangyi Yan, Xiaohui Xie
Although having achieved great success in medical image segmentation, deep convolutional neural networks usually require a large dataset with manual annotations for training and are difficult to generalize to unseen classes.
no code implementations • 16 Dec 2020 • Hao Tang, Xingwei Liu, Kun Han, Shanlin Sun, Narisu Bai, Xuming Chen, Huang Qian, Yong liu, Xiaohui Xie
State-of-the-art CNN segmentation models apply either 2D or 3D convolutions on input images, with pros and cons associated with each method: 2D convolution is fast, less memory-intensive but inadequate for extracting 3D contextual information from volumetric images, while the opposite is true for 3D convolution.
no code implementations • 23 Mar 2019 • Hao Tang, Xingwei Liu, Xiaohui Xie
Most of the existing deep learning nodule detection systems are constructed in two steps: a) nodule candidates screening and b) false positive reduction, using two different models trained separately.