Description: 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
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