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
this https URL

本文提出了一种新颖的联合学习和密集协作融合（JL-DCF）架构，用于 RGB-D 显着对象检测。通过 Siamese 网络，JL-DCF 从 RGB 和深度输入中学习，提供了稳健的显着特征学习和互补特征发现。在四个流行计量标准上全面实验表明，该框架具有良好的普适性和鲁棒性，并提供了实际应用的潜在解决方案。