Abstract
Effective feature representations which should not
only express the image’s individual properties, but
also reflect the interaction among group images
are essentially crucial for robust co-saliency detection. This paper proposes a novel deep learning co-saliency detection approach which simultaneously learns single image properties and robust
group feature in a recurrent manner. Specifically,
our network first extracts the semantic features of
each image. Then, a specially designed Recurrent
Co-Attention Unit (RCAU) will explore all images
in the group recurrently to generate the final group
representation using the co-attention between images, and meanwhile suppresses noisy information.
The group feature which contains complementary
synergetic information is later merged with the single image features which express the unique properties to infer robust co-saliency. We also propose
a novel co-perceptual loss to make full use of interactive relationships of whole images in the training
group as the supervision in our end-to-end training process. Extensive experimental results demonstrate the superiority of our approach in comparison
with the state-of-the-art methods