102 citations · 197 across the 7 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Shuai Li, Wanqing Li, Chris Cook +2
Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and ex…
Foreground Detection in Camouflaged Scenes
Shuai Li, Dinei Florencio, Yaqin Zhao +2
Foreground detection has been widely studied for decades due to its importance in many practical applications. Most of the existing methods assume foreground and background show vi…