activity
20142022
most citedIndependently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN

125 citations · 631 across the 26 of their papers we have counts for

collaborators

26 papers

physics.flu-dyn2022★ 43 cited

Electric field enhancement of pool boiling of dielectric fluids on pillar-structured surfaces: A lattice Boltzmann study

Wanxin Li, Qing Li, Yue Yu +1

In this paper, by using a phase-change lattice Boltzmann (LB) model coupled with an electric field model, we numerically investigate the performance and enhancement mechanism of po…

cs.DC2019★ 102 cited

Security modeling and efficient computation offloading for service workflow in mobile edge computing

Binbin Huang, Zhongjin Lia, Peng Tang +5

It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge…

cs.CV2018★ 16 cited

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…

cs.CV2018★ 69 cited

A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain

Shuai Li, Dinei Florencio, Wanqing Li +2

Detecting camouflaged moving foreground objects has been known to be difficult due to the similarity between the foreground objects and the background. Conventional methods cannot…

cs.CV2018★ 20 cited

Importance Weighted Adversarial Nets for Partial Domain Adaptation

Jing Zhang, Zewei Ding, Wanqing Li +1

This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less…

cs.CV2018★ 1 cited

Unsupervised Domain Adaptation: A Multi-task Learning-based Method

Jing Zhang, Wanqing Li, Philip Ogunbona

This paper presents a novel multi-task learning-based method for unsupervised domain adaptation. Specifically, the source and target domain classifiers are jointly learned by consi…