  This paper proposes a novel joint learning and densely-cooperative fusion
(JL-DCF) architecture for RGB-D salient object detection. Existing models
usually treat RGB and depth as independent information and design separate
networks for feature extraction from each. Such schemes can easily be
constrained by a limited amount of training data or over-reliance on an
elaborately-designed training process. In contrast, our JL-DCF learns from both
RGB and depth inputs through a Siamese network. To this end, we propose two
effective components: joint learning (JL), and densely-cooperative fusion
(DCF). The JL module provides robust saliency feature learning, while the
latter is introduced for complementary feature discovery. Comprehensive
experiments on four popular metrics show that the designed framework yields a
robust RGB-D saliency detector with good generalization. As a result, JL-DCF
significantly advances the top-1 D3Net model by an average of ~1.9% (S-measure)
across six challenging datasets, showing that the proposed framework offers a
potential solution for real-world applications and could provide more insight
into the cross-modality complementarity task. The code will be available at
https://github.com/kerenfu/JLDCF/.
