MambaSOD：用于RGB-D显著目标检测的双Mamba驱动跨模态融合网络
MambaSOD: Dual Mamba-Driven Cross-Modal Fusion Network for RGB-D Salient Object Detection
AI总结：
本文提出MambaSOD，首次将Mamba引入RGB-D显著目标检测，通过双Mamba特征提取器和跨模态融合Mamba以线性复杂度建模长距离依赖并有效融合RGB与深度互补信息，在六个数据集上超越十六个SOTA模型。
摘要：
RGB-D显著目标检测（SOD）的目的是准确定位图像中最具视觉显著性的区域。传统深度模型严重依赖CNN提取器，忽视了长距离上下文依赖关系；随后基于Transformer的模型在一定程度上解决了这一问题，但引入了较高的计算复杂度。此外，融合深度图的空间信息已被证明对该任务有效。该问题的一个主要挑战是如何有效融合来自RGB和深度模态的互补信息。本文提出了一种用于RGB-D SOD的双Mamba驱动跨模态融合网络，命名为MambaSOD。具体而言，我们首先对RGB和深度输入分别采用双Mamba驱动的特征提取器，以线性复杂度建模多模态输入中的长距离依赖关系。然后，我们为捕获的多模态特征设计了一个跨模态融合Mamba，以充分利用RGB特征与深度特征之间的互补信息。据我们所知，本工作是首次尝试探索Mamba在RGB-D SOD任务中的潜力，提供了一个新的视角。在六个主流数据集上进行的大量实验表明，我们的方法优于十六个最先进的RGB-D SOD模型。源代码将在https://github.com/YueZhan721/MambaSOD发布。
英文摘要：
The purpose of RGB-D Salient Object Detection (SOD) is to pinpoint the most visually conspicuous areas within images accurately. While conventional deep models heavily rely on CNN extractors and overlook the long-range contextual dependencies, subsequent transformer-based models have addressed the issue to some extent but introduce high computational complexity. Moreover, incorporating spatial information from depth maps has been proven effective for this task. A primary challenge of this issue is how to fuse the complementary information from RGB and depth effectively. In this paper, we propose a dual Mamba-driven cross-modal fusion network for RGB-D SOD, named MambaSOD. Specifically, we first employ a dual Mamba-driven feature extractor for both RGB and depth to model the long-range dependencies in multiple modality inputs with linear complexity. Then, we design a cross-modal fusion Mamba for the captured multi-modal features to fully utilize the complementary information between the RGB and depth features. To the best of our knowledge, this work is the first attempt to explore the potential of the Mamba in the RGB-D SOD task, offering a novel perspective. Numerous experiments conducted on six prevailing datasets demonstrate our method's superiority over sixteen state-of-the-art RGB-D SOD models. The source code will be released at https://github.com/YueZhan721/MambaSOD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
