64 citations · 117 across the 13 of their papers we have counts for
13 papers
Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks
Xiaohan Liu, Yanwei Pang, Xuebin Sun +4
Partial scan is a common approach to accelerate Magnetic Resonance Imaging (MRI) data acquisition in both 2D and 3D settings. However, accurately reconstructing images from partial…
FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis
Zihui Guo, Yonghong Hou, Pichao Wang +3
Analysis of human interaction is one important research topic of human motion analysis. It has been studied either using first person vision (FPV) or third person vision (TPV). How…
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning
Haoyuan Zhang, Yonghong Hou, Wenjing Zhang +1
Recent contrastive based 3D action representation learning has made great progress. However, the strict positive/negative constraint is yet to be relaxed and the use of non-self po…
Learning Using Privileged Information for Zero-Shot Action Recognition
Zhiyi Gao, Yonghong Hou, Wanqing Li +2
Zero-Shot Action Recognition (ZSAR) aims to recognize video actions that have never been seen during training. Most existing methods assume a shared semantic space between seen and…
A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition
Shuangyan Miao, Yonghong Hou, Zhimin Gao +2
This paper proposes a new graph convolutional operator called central difference graph convolution (CDGC) for skeleton based action recognition. It is not only able to aggregate no…
Trear: Transformer-based RGB-D Egocentric Action Recognition
Xiangyu Li, Yonghong Hou, Pichao Wang +3
In this paper, we propose a \textbf{Tr}ansformer-based RGB-D \textbf{e}gocentric \textbf{a}ction \textbf{r}ecognition framework, called Trear. It consists of two modules, inter-fra…
Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry
Xiangyu Li, Yonghong Hou, Pichao Wang +3
Existing unsupervised visual odometry (VO) methods either match pairwise images or integrate the temporal information using recurrent neural networks over a long sequence of images…
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching
Haoyuan Zhang, Yonghong Hou, Pichao Wang +2
This paper presents a study of automatic design of neural network architectures for skeleton-based action recognition. Specifically, we encode a skeleton-based action instance into…
Skeleton-based Action Recognition Using LSTM and CNN
Chuankun Li, Pichao Wang, Shuang Wang +2
Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation. With the development of deep…
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks
Pichao Wang, Wanqing Li, Chuankun Li +1
Convolutional Neural Networks (ConvNets) have recently shown promising performance in many computer vision tasks, especially image-based recognition. How to effectively apply ConvN…
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
Pichao Wang, Zhaoyang Li, Yonghong Hou +1
Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition. How to effectively use ConvN…
Combining ConvNets with Hand-Crafted Features for Action Recognition Based on an HMM-SVM Classifier
Pichao Wang, Zhaoyang Li, Yonghong Hou +1
This paper proposes a new framework for RGB-D-based action recognition that takes advantages of hand-designed features from skeleton data and deeply learned features from depth map…
Online Action Recognition based on Incremental Learning of Weighted Covariance Descriptors
Chang Tang, Pichao Wang, Wanqing Li
Different from traditional action recognition based on video segments, online action recognition aims to recognize actions from unsegmented streams of data in a continuous manner.…