Samba+: 通过更统一的Mamba框架实现通用且准确的显著目标检测
Samba+: General and Accurate Salient Object Detection via A More Unified Mamba-based Framework
AI总结：
Samba+通过统一的Mamba框架实现多模态显著目标检测，提升模型通用性和准确性。
摘要：
现有的显著目标检测（SOD）模型通常受到卷积神经网络（CNNs）有限感受野和Transformer二次计算复杂度的限制。最近，新兴的状态空间模型，即Mamba，展示了在平衡全局感受野和计算效率方面的巨大潜力。作为解决方案，我们提出了Saliency Mamba（Samba），一种纯基于Mamba的架构，能够灵活处理各种不同的SOD任务，包括RGB/RGB-D/RGB-T SOD、视频SOD（VSOD）、RGB-D VSOD以及可见-深度-热SOD。具体而言，我们重新思考了Mamba在SOD中的扫描策略，并引入了具有空间邻域扫描（SNS）算法的显著引导Mamba块（SGMB），以保留显著区域的空间连续性。此外，还提出了一种上下文感知上采样（CAU）方法，通过建模上下文依赖性来促进层次特征对齐和聚合。作为进一步的步骤，为了避免以往SOD解决方案中的
英文摘要：
Existing salient object detection (SOD) models are generally constrained by the limited receptive fields of convolutional neural networks (CNNs) and quadratic computational complexity of Transformers. Recently, the emerging state-space model, namely Mamba, has shown great potential in balancing global receptive fields and computational efficiency. As a solution, we propose Saliency Mamba (Samba), a pure Mamba-based architecture that flexibly handles various distinct SOD tasks, including RGB/RGB-D/RGB-T SOD, video SOD (VSOD), RGB-D VSOD, and visible-depth-thermal SOD. Specifically, we rethink the scanning strategy of Mamba for SOD, and introduce a saliency-guided Mamba block (SGMB) that features a spatial neighborhood scanning (SNS) algorithm to preserve the spatial continuity of salient regions. A context-aware upsampling (CAU) method is also proposed to promote hierarchical feature alignment and aggregation by modeling contextual dependencies. As one step further, to avoid the "task-specific" problem as in previous SOD solutions, we develop Samba+, which is empowered by training Samba in a multi-task joint manner, leading to a more unified and versatile model. Two crucial components that collaboratively tackle challenges encountered in input of arbitrary modalities and continual adaptation are investigated. Specifically, a hub-and-spoke graph attention (HGA) module facilitates adaptive cross-modal interactive fusion, and a modality-anchored continual learning (MACL) strategy alleviates inter-modal conflicts together with catastrophic forgetting. Extensive experiments demonstrate that Samba individually outperforms existing methods across six SOD tasks on 22 datasets with lower computational cost, whereas Samba+ achieves even superior results on these tasks and datasets by using a single trained versatile model. Additional results further demonstrate the potential of our Samba framework.
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。
