102 citations · 197 across the 7 of their papers we have counts for
7 papers · 1 filter
Learning Approximate Stochastic Transition Models
Yuhang Song, Christopher Grimm, Xianming Wang +1
We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states an…
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…
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…
A Fully Trainable Network with RNN-based Pooling
Shuai Li, Wanqing Li, Chris Cook +2
Pooling is an important component in convolutional neural networks (CNNs) for aggregating features and reducing computational burden. Compared with other components such as convolu…
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
Jing Zhang, Wanqing Li, Philip Ogunbona
This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition. We propose a unified framework that reduces the shift between domains both st…
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
Zewei Ding, Pichao Wang, Philip O. Ogunbona +1
Deep learning techniques are being used in skeleton based action recognition tasks and outstanding performance has been reported. Compared with RNN based methods which tend to over…