The popularity and promotion of depth maps have brought new vigor and
vitality into salient object detection (SOD), and a mass of RGB-D SOD
algorithms have been proposed, mainly concentrating on how to better integrate
cross-modality features from RGB image and depth map. For the cross-modality
interaction in feature encoder, existing methods either indiscriminately treat
RGB and depth modalities, or only habitually utilize depth cues as auxiliary
information of the RGB branch. Different from them, we reconsider the status of
two modalities and propose a novel Cross-modality Discrepant Interaction
Network (CDINet) for RGB-D SOD, which differentially models the dependence of
two modalities according to the feature representations of different layers. To
this end, two components are designed to implement the effective cross-modality
interaction: 1) the RGB-induced Detail Enhancement (RDE) module leverages RGB
modality to enhance the details of the depth features in low-level encoder
stage. 2) the Depth-induced Semantic Enhancement (DSE) module transfers the
object positioning and internal consistency of depth features to the RGB branch
in high-level encoder stage. Furthermore, we also design a Dense Decoding
Reconstruction (DDR) structure, which constructs a semantic block by combining
multi-level encoder features to upgrade the skip connection in the feature
decoding. Extensive experiments on five benchmark datasets demonstrate that our
network outperforms $15$ state-of-the-art methods both quantitatively and
qualitatively. Our code is publicly available at:
this https URL

通过重新考虑两种模态的重要性，本文提出了一种新的跨模态矛盾交互网络（CDINet），用于 RGB-D SOD。实验结果表明，这种网络在定量和定性上都优于 15 种现有的方法。