64 citations · 227 across the 14 of their papers we have counts for
15 papers
OpenJev-RLCD: A Working RLCD Implementation
Zhimin Gao, Pichao Wang
Decision models such as Jev answer questions with probabilities, which are only useful if they are calibrated. Open-source reproductions rely on supervised fine-tuning plus tempera…
Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey
Lijuan Zhou, Xiang Meng, Zhihuan Liu +3
Human pose analysis has garnered significant attention within both the research community and practical applications, owing to its expanding array of uses, including gaming, video…
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition
Zhimin Gao, Peitao Wang, Pei Lv +5
Despite great progress achieved by transformer in various vision tasks, it is still underexplored for skeleton-based action recognition with only a few attempts. Besides, these met…
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…
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…
MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects
Lisha Cui, Rui Ma, Pei Lv +4
For most of the object detectors based on multi-scale feature maps, the shallow layers are rich in fine spatial information and thus mainly responsible for small object detection.…
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks
Pichao Wang, Wanqing Li, Zhimin Gao +2
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDN…
Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks
Pichao Wang, Wanqing Li, Zhimin Gao +3
Scene flow describes the motion of 3D objects in real world and potentially could be the basis of a good feature for 3D action recognition. However, its use for action recognition,…
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
Pichao Wang, Wanqing Li, Song Liu +3
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDN…
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
Pichao Wang, Wanqing Li, Song Liu +3
This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets). The proposed method first segments…
HEp-2 Cell Image Classification with Deep Convolutional Neural Networks
Zhimin Gao, Lei Wang, Luping Zhou +1
Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper presents an automatic framework for this classif…
Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences
Pichao Wang, Wanqing Li, Zhimin Gao +3
Recently, deep learning approach has achieved promising results in various fields of computer vision. In this paper, a new framework called Hierarchical Depth Motion Maps (HDMM) +…
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
Pichao Wang, Wanqing Li, Philip Ogunbona +2
Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discrimin…