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arXiv · 1705.01921

Recurrent Soft Attention Model for Common Object Recognition

Liliang Ren
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Abstract

We propose the Recurrent Soft Attention Model, which integrates the visual attention from the original image to a LSTM memory cell through a down-sample network. The model recurrently transmits visual attention to the memory cells for glimpse mask generation, which is a more natural way for attention integration and exploitation in general object detection and recognition problem. We test our model under the metric of the top-1 accuracy on the CIFAR-10 dataset. The experiment shows that our down-sample network and feedback mechanism plays an effective role among the whole network structure.

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Liliang Ren. 2017-05-04. Recurrent Soft Attention Model for Common Object Recognition. https://arxiv.org/abs/1705.01921

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