As one of the tasks in Image Fusion, Infrared and Visible Image Fusion aims
to integrate complementary information captured by sensors of different
modalities into a single image. The Selective State Space Model (SSSM), known
for its ability to capture long-range dependencies, has demonstrated its
potential in the field of computer vision. However, in image fusion, current
methods underestimate the potential of SSSM in capturing the global spatial
information of both modalities. This limitation prevents the simultaneous
consideration of the global spatial information from both modalities during
interaction, leading to a lack of comprehensive perception of salient targets.
Consequently, the fusion results tend to bias towards one modality instead of
adaptively preserving salient targets. To address this issue, we propose the
Saliency-aware Selective State Space Fusion Model (S4Fusion). In our S4Fusion,
the designed Cross-Modal Spatial Awareness Module (CMSA) can simultaneously
focus on global spatial information from both modalities while facilitating
their interaction, thereby comprehensively capturing complementary information.
Additionally, S4Fusion leverages a pre-trained network to perceive uncertainty
in the fused images. By minimizing this uncertainty, S4Fusion adaptively
highlights salient targets from both images. Extensive experiments demonstrate
that our approach produces high-quality images and enhances performance in
downstream tasks.

为了解决现有方法对全局空间信息的捕捉能力不足的问题，本文提出了一种考虑全局空间信息的显著性感知选择性状态空间融合模型（S4Fusion），通过设计跨模态空间意识模块（CMSA）和利用预训练网络感知融合图像中的不确定性来全面捕捉补充信息，并自适应地突出两幅图像中的显著目标，实验结果表明该方法能够产生高质量的图像并提升下游任务的性能。

S4Fusion：红外 - 可见图像融合的关注显著性选择性状态空间模型

S4Fusion: Saliency-aware Selective State Space Model for Infrared  Visible Image Fusion

Deep learning-based sequence models are extensively employed in Time Series
Anomaly Detection (TSAD) tasks due to their effective sequential modeling
capabilities. However, the ability of TSAD is limited by two key challenges:
(i) the ability to model long-range dependency and (ii) the generalization
issue in the presence of non-stationary data. To tackle these challenges, an
anomaly detector that leverages the selective state space model known for its
proficiency in capturing long-term dependencies across various domains is
proposed. Additionally, a multi-stage detrending mechanism is introduced to
mitigate the prominent trend component in non-stationary data to address the
generalization issue. Extensive experiments conducted on realworld public
datasets demonstrate that the proposed methods surpass all 12 compared baseline
methods.

通过利用选择性状态空间模型和多阶段去趋势机制，该论文提出了一种用于时间序列异常检测的深度学习模型，能够有效地建模长程依赖关系并解决非平稳数据的泛化问题，实证实验表明该方法优于其他 12 个对比基准方法。

联合选择状态空间模型和去趋势处理的鲁棒时间序列异常检测

Joint Selective State Space Model and Detrending for Robust Time Series  Anomaly Detection

Effectively discerning spatial-spectral dependencies in HSI denoising is
crucial, but prevailing methods using convolution or transformers still face
computational efficiency limitations. Recently, the emerging Selective State
Space Model(Mamba) has risen with its nearly linear computational complexity in
processing natural language sequences, which inspired us to explore its
potential in handling long spectral sequences. In this paper, we propose
HSIDMamba(HSDM), tailored to exploit the linear complexity for effectively
capturing spatial-spectral dependencies in HSI denoising. In particular, HSDM
comprises multiple Hyperspectral Continuous Scan Blocks, incorporating
BCSM(Bidirectional Continuous Scanning Mechanism), scale residual, and spectral
attention mechanisms to enhance the capture of long-range and local
spatial-spectral information. BCSM strengthens spatial-spectral interactions by
linking forward and backward scans and enhancing information from eight
directions through SSM, significantly enhancing the perceptual capability of
HSDM and improving denoising performance more effectively. Extensive
evaluations against HSI denoising benchmarks validate the superior performance
of HSDM, achieving state-of-the-art results in performance and surpassing the
efficiency of the latest transformer architectures by $30\%$.

通过利用选择性状态空间模型（Mamba）的近乎线性计算复杂性，本文提出了 HSIDMamba（HSDM）用于高光谱图像去噪，利用 HSDM 的连续扫描机制强化了空间 - 光谱相互作用，提高了去噪性能，实验结果表明 HSDM 在性能上超过了最新的变压器架构的效率提升 30%。