  Typically, objects with the same semantics are not always prominent in images
containing different backgrounds. Motivated by this observation that accurately
salient object detection is related to both foreground and background, we
proposed a novel cooperative attention mechanism that jointly considers
reciprocal relationships between background and foreground for efficient
salient object detection. Concretely, we first aggregate the features at each
side-out of traditional dilated FCN to extract the initial foreground and
background local responses respectively. Then taking these responses as input,
reciprocal attention module adaptively models the nonlocal dependencies between
any two pixels of the foreground and background features, which is then
aggregated with local features in a mutual reinforced way so as to enhance each
branch to generate more discriminative foreground and background saliency map.
Besides, cooperative losses are particularly designed to guide the multi-task
learning of foreground and background branches, which encourages our network to
obtain more complementary predictions with clear boundaries. At last, a simple
but effective fusion strategy is utilized to produce the final saliency map.
Comprehensive experimental results on five benchmark datasets demonstrate that
our proposed method performs favorably against the state-of-the-art approaches
in terms of all compared evaluation metrics.
