  Existing RGB-D salient object detection (SOD) 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. Inspired by the observation that RGB and depth modalities actually
present certain commonality in distinguishing salient objects, a novel joint
learning and densely cooperative fusion (JL-DCF) architecture is designed to
learn from both RGB and depth inputs through a shared network backbone, known
as the Siamese architecture. In this paper, we propose two effective
components: joint learning (JL), and densely cooperative fusion (DCF). The JL
module provides robust saliency feature learning by exploiting cross-modal
commonality via a Siamese network, while the DCF module is introduced for
complementary feature discovery. Comprehensive experiments using five 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 state-of-the-art models by an average of ~2.0% (max F-measure) across seven
challenging datasets. In addition, we show that JL-DCF is readily applicable to
other related multi-modal detection tasks, including RGB-T (thermal infrared)
SOD and video SOD, achieving comparable or even better performance against
state-of-the-art methods. We also link JL-DCF to the RGB-D semantic
segmentation field, showing its capability of outperforming several semantic
segmentation models on the task of RGB-D SOD. These facts further confirm that
the proposed framework could offer a potential solution for various
applications and provide more insight into the cross-modal complementarity
task.
