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Found 5 papers, 2 papers with code
Deep Independently Recurrent Neural Network (IndRNN)
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.
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
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
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.
Ranked #10 on
Language Modelling
on Penn Treebank (Character Level)
Foreground Detection in Camouflaged Scenes
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
Such a network with learnable pooling function is referred to as a fully trainable network (FTN).
