125 citations · 230 across the 5 of their papers we have counts for
5 papers
Deep Independently Recurrent Neural Network (IndRNN)
Shuai Li, Wanqing Li, Chris Cook +1
Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient vanishing and exploding problems and thus difficult to learn long-term patterns and construc…
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…
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…
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…