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