Recurrent Attentional Networks for Saliency Detection

Jason Kuen; Zhenhua Wang; Gang Wang

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arXiv · 1604.03227

Recurrent Attentional Networks for Saliency Detection

Jason Kuen·Zhenhua Wang·Gang Wang

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Abstract

Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent network units, RACDNN is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively. Besides tackling the scale problem, RACDNN can also learn context-aware features from past iterations to enhance saliency refinement in future iterations. Experiments on several challenging saliency detection datasets validate the effectiveness of RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection methods.

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Jason Kuen, Zhenhua Wang, Gang Wang. 2016-04-12. Recurrent Attentional Networks for Saliency Detection. https://arxiv.org/abs/1604.03227

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