Recurrent Double Features: Recurrent Multi scale Deep Features and Saliency Features for Salient Object Detection
Fully convolutional networks can be used to perform end to end saliency detection, but pooling operations generate low resolution results. We propose a novel recurrent architecture to generate and to refine the saliency maps. We firstly train a deep FCN (fully convolutional networks) to extract multi deep features and saliency features(denote as doublefeatures) then we feed the doublefeatures into the recurrent FCN. After each loop, the recurrent architecture generates new saliency features and saliency maps with higher resolutions and quality. We evaluate our method on many datasets, and our RDF outperforms state of the art saliency detection methods.
PCM