Search Results for author: Chris Cook

Found 5 papers, 2 papers with code

Deep Independently Recurrent Neural Network (IndRNN)

1 code implementation11 Oct 2019 Shuai Li, Wanqing Li, Chris Cook, Yanbo Gao

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 construct deep networks.

Language Modelling Sequential Image Classification +1

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

no code implementations16 Apr 2018 Shuai Li, Dinei Florencio, Wanqing Li, Yaqin Zhao, Chris Cook

Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects.

Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN

11 code implementations CVPR 2018 Shuai Li, Wanqing Li, Chris Cook, Ce Zhu, Yanbo Gao

Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly.

Language Modelling Sequential Image Classification +1

Foreground Detection in Camouflaged Scenes

no code implementations11 Jul 2017 Shuai Li, Dinei Florencio, Yaqin Zhao, Chris Cook, Wanqing Li

This paper proposes a texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes.

A Fully Trainable Network with RNN-based Pooling

no code implementations16 Jun 2017 Shuai Li, Wanqing Li, Chris Cook, Ce Zhu, Yanbo Gao

Such a network with learnable pooling function is referred to as a fully trainable network (FTN).

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