RSCaMa：基于状态空间模型的遥感图像变化描述
RSCaMa: Remote Sensing Image Change Captioning with State Space Model
AI总结：
针对遥感图像变化描述任务中联合时空建模的不足，提出基于Mamba状态空间模型的RSCaMa模型，通过SD-SSM和TT-SSM分别实现高效空间与时间建模，性能优异并对比了三种语言解码器。
摘要：
遥感图像变化描述（Remote Sensing Image Change Captioning, RSICC）旨在用语言描述多时相遥感图像之间的地表变化，包括变化的目标类别、位置以及变化目标的动态（例如新增或消失）。这对双时相特征的时空建模提出了挑战。尽管已有方法在空间变化感知方面取得了进展，但在联合时空建模方面仍存在不足。为解决这一问题，本文提出了一种新型RSCaMa模型，该模型通过多个CaMa层实现高效的联合时空建模，能够对双时相特征进行迭代细化。为实现高效的空间建模，我们将近期流行的、具备全局感受野和线性复杂度的Mamba（一种状态空间模型）引入RSICC任务，并提出空间差异感知状态空间模型（Spatial Difference-aware SSM, SD-SSM），克服了以往基于CNN和Transformer的方法在感受野与计算复杂度方面的局限。SD-SSM显著增强了模型捕捉空间变化的能力。在高效时间建模方面，考虑到Mamba的时间扫描特性与RSICC的时间性之间的潜在关联，我们提出时间遍历状态空间模型（Temporal-Traversing SSM, TT-SSM），该模型以时间交叉方式扫描双时相特征，增强了模型的时间理解与信息交互能力。实验验证了高效联合时空建模的有效性，证明了RSCaMa的优异性能以及Mamba在RSICC任务中的潜力。此外，我们系统比较了三种不同的语言解码器，包括Mamba、GPT风格解码器和Transformer解码器，为未来的RSICC研究提供了有价值的见解。代码将发布于https://github.com/Chen-Yang-Liu/RSCaMa。
英文摘要：
Remote Sensing Image Change Captioning (RSICC) aims to describe surface changes between multi-temporal remote sensing images in language, including the changed object categories, locations, and dynamics of changing objects (e.g., added or disappeared). This poses challenges to spatial and temporal modeling of bi-temporal features. Despite previous methods progressing in the spatial change perception, there are still weaknesses in joint spatial-temporal modeling. To address this, in this paper, we propose a novel RSCaMa model, which achieves efficient joint spatial-temporal modeling through multiple CaMa layers, enabling iterative refinement of bi-temporal features. To achieve efficient spatial modeling, we introduce the recently popular Mamba (a state space model) with a global receptive field and linear complexity into the RSICC task and propose the Spatial Difference-aware SSM (SD-SSM), overcoming limitations of previous CNN- and Transformer-based methods in the receptive field and computational complexity. SD-SSM enhances the model's ability to capture spatial changes sharply. In terms of efficient temporal modeling, considering the potential correlation between the temporal scanning characteristics of Mamba and the temporality of the RSICC, we propose the Temporal-Traversing SSM (TT-SSM), which scans bi-temporal features in a temporal cross-wise manner, enhancing the model's temporal understanding and information interaction. Experiments validate the effectiveness of the efficient joint spatial-temporal modeling and demonstrate the outstanding performance of RSCaMa and the potential of the Mamba in the RSICC task. Additionally, we systematically compare three different language decoders, including Mamba, GPT-style decoder, and Transformer decoder, providing valuable insights for future RSICC research. The code will be available at \emph{\url{https://github.com/Chen-Yang-Liu/RSCaMa}}
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。
