Search Results for author: Xingwei Liu

Found 6 papers, 4 papers with code

Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

1 code implementation22 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.

PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation

1 code implementation16 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 #16 on 3D Human Pose Estimation on Human3.6M (using extra training data)

2D Human Pose Estimation 3D Human Pose Estimation

TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation

1 code implementation18 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 #19 on 3D Human Pose Estimation on Human3.6M (using extra training data)

3D Human Pose Estimation 3D Pose Estimation

Recurrent Mask Refinement for Few-Shot Medical Image Segmentation

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.

Few-Shot Learning Image Segmentation +4

Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation

no code implementations16 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.

Image Segmentation Organ Segmentation +2

An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction

no code implementations23 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.

Computed Tomography (CT)

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