Salient object detection (SOD) on RGB and depth images has attracted more and
more research interests, due to its effectiveness and the fact that depth cues
can now be conveniently captured. Existing RGB-D SOD models usually adopt
different fusion strategies to learn a shared representation from the two
modalities (\ie, RGB and depth), while few methods explicitly consider how to
preserve modality-specific characteristics. In this study, we propose a novel
framework, termed SPNet} (Specificity-preserving network), which benefits SOD
performance by exploring both the shared information and modality-specific
properties (\eg, specificity). Specifically, we propose to adopt two
modality-specific networks and a shared learning network to generate individual
and shared saliency prediction maps, respectively. To effectively fuse
cross-modal features in the shared learning network, we propose a
cross-enhanced integration module (CIM) and then propagate the fused feature to
the next layer for integrating cross-level information. Moreover, to capture
rich complementary multi-modal information for boosting the SOD performance, we
propose a multi-modal feature aggregation (MFA) module to integrate the
modality-specific features from each individual decoder into the shared
decoder. By using a skip connection, the hierarchical features between the
encoder and decoder layers can be fully combined. Extensive experiments
demonstrate that our~\ours~outperforms cutting-edge approaches on six popular
RGB-D SOD and three camouflaged object detection benchmarks. The project is
publicly available at: this https URL

该研究提出了一种称为 SPNet 的新框架，它通过探索共享信息和特定性质（如特定性）来受益于 SOD 性能，并采用双模态特定网络和共同学习网络来生成单独的和共享的显着性预测地图，分别。此外，为了捕获丰富的互补多模态信息以提高 SOD 性能，该研究还提出了一种多模态特征聚合（MFA）模块。

保留特异性的 RGB-D 显著性检测

Specificity-preserving RGB-D Saliency Detection

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。

RGB-D 显著目标检测的跨模态不一致交互网络

Cross-modality Discrepant Interaction Network for RGB-D Salient Object  Detection

Most existing RGB-D salient object detection (SOD) methods focus on the
foreground region when utilizing the depth images. However, the background also
provides important information in traditional SOD methods for promising
performance. To better explore salient information in both foreground and
background regions, this paper proposes a Bilateral Attention Network (BiANet)
for the RGB-D SOD task. Specifically, we introduce a Bilateral Attention Module
(BAM) with a complementary attention mechanism: foreground-first (FF) attention
and background-first (BF) attention. The FF attention focuses on the foreground
region with a gradual refinement style, while the BF one recovers potentially
useful salient information in the background region. Benefitted from the
proposed BAM module, our BiANet can capture more meaningful foreground and
background cues, and shift more attention to refining the uncertain details
between foreground and background regions. Additionally, we extend our BAM by
leveraging the multi-scale techniques for better SOD performance. Extensive
experiments on six benchmark datasets demonstrate that our BiANet outperforms
other state-of-the-art RGB-D SOD methods in terms of objective metrics and
subjective visual comparison. Our BiANet can run up to 80fps on $224\times224$
RGB-D images, with an NVIDIA GeForce RTX 2080Ti GPU. Comprehensive ablation
studies also validate our contributions.

本文提出了一种双边注意力网络 (BiANet) 来探索前景和背景区域中的显著信息，其中介绍了一种双边注意力模块 (BAM)，通过前景优先和背景优先的注意机制来获取前景和背景信息，以更好地优化 RGB-D 任务的性能。基于多尺度和 BAM 技术，我们的 BiANet 在六个基准数据集上实现了显著优越的性能。