activity
20192021
most citedUnderstanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack

8 citations · 18 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV20218 cited

Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack

He Wang, Feixiang He, Zhexi Peng +4

Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we exami…

cs.CV20212 cited

Enhanced 3D Human Pose Estimation from Videos by using Attention-Based Neural Network with Dilated Convolutions

Ruixu Liu, Ju Shen, He Wang +3

The attention mechanism provides a sequential prediction framework for learning spatial models with enhanced implicit temporal consistency. In this work, we show a systematic desig…

cs.CV2021

In-game Residential Home Planning via Visual Context-aware Global Relation Learning

Lijuan Liu, Yin Yang, Yi Yuan +3

In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home comp…

cs.LG20214 cited

High-order Differentiable Autoencoder for Nonlinear Model Reduction

Siyuan Shen, Yang Yin, Tianjia Shao +4

This paper provides a new avenue for exploiting deep neural networks to improve physics-based simulation. Specifically, we integrate the classic Lagrangian mechanics with a deep au…

cs.CV20204 cited

Dynamic Future Net: Diversified Human Motion Generation

Wenheng Chen, He Wang, Yi Yuan +2

Human motion modelling is crucial in many areas such as computer graphics, vision and virtual reality. Acquiring high-quality skeletal motions is difficult due to the need for spec…

cs.CV2019

SMART: Skeletal Motion Action Recognition aTtack

He Wang, Feixiang He, Zhexi Peng +4

Adversarial attack has inspired great interest in computer vision, by showing that classification-based solutions are prone to imperceptible attack in many tasks. In this paper, we…