Pan-sharpening involves integrating information from lowresolution
multi-spectral and high-resolution panchromatic images to generate
high-resolution multi-spectral counterparts. While recent advancements in the
state space model, particularly the efficient long-range dependency modeling
achieved by Mamba, have revolutionized computer vision community, its untapped
potential in pan-sharpening motivates our exploration. Our contribution,
Pan-Mamba, represents a novel pansharpening network that leverages the
efficiency of the Mamba model in global information modeling. In Pan-Mamba, we
customize two core components: channel swapping Mamba and cross-modal Mamba,
strategically designed for efficient cross-modal information exchange and
fusion. The former initiates a lightweight cross-modal interaction through the
exchange of partial panchromatic and multispectral channels, while the latter
facilities the information representation capability by exploiting inherent
cross-modal relationships. Through extensive experiments across diverse
datasets, our proposed approach surpasses state-of-theart methods, showcasing
superior fusion results in pan-sharpening. To the best of our knowledge, this
work is the first attempt in exploring the potential of the Mamba model and
establishes a new frontier in the pan-sharpening techniques. The source code is
available at this https URL .

本研究提出一种使用 Mamba 模型进行全局信息建模的新型 pan-sharpening 网络 ——Pan-Mamba，通过轻量级的交叉模态交互和利用内在的跨模态关系，实现高效的信息表示和融合，并在各种数据集上超过了现有方法，在 pan-sharpening 领域取得了卓越的融合结果，是对 Mamba 模型潜力的首次探索和全新的 pan-sharpening 技术前沿的建立。

Pan-Mamba: 有效的全色融合方法与状态空间模型

Pan-Mamba: Effective pan-sharpening with State Space Model

Parameter Efficient Tuning (PET) has gained attention for reducing the number
of parameters while maintaining performance and providing better hardware
resource savings, but few studies investigate dense prediction tasks and
interaction between modalities. In this paper, we do an investigation of
efficient tuning problems on referring image segmentation. We propose a novel
adapter called Bridger to facilitate cross-modal information exchange and
inject task-specific information into the pre-trained model. We also design a
lightweight decoder for image segmentation. Our approach achieves comparable or
superior performance with only 1.61\% to 3.38\% backbone parameter updates,
evaluated on challenging benchmarks. The code is available at
https://github.com/kkakkkka/ETRIS.

我们在密集预测任务和模态之间的交互方面对参数高效调整进行了调查，并且提出了一种名为 Bridger 的新型适配器，以促进跨模态信息交流并将任务特定信息注入预训练模型，并为图像分割设计了轻量级解码器，该方法在具有挑战性的基准测试中只更新了 1.61％至 3.38％的主干参数，并实现了相当或更高的性能。