通过引导输入和推理链解锁多光谱数据以多模态模型
Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning
AI总结：
本文提出一种无需训练的方法，将多光谱数据引入标准RGB-only LMMs的推理流程中，提升性能。通过适应非RGB输入并注入领域特定信息和推理链推理，实现多模态模型在遥感任务中的零样本性能提升。
摘要：
多光谱影像是遥感应用中的宝贵输入信号，如土地利用和土地覆盖分类及环境监测。然而，通用的大型多模态模型（LMMs）通常在RGB图像上训练，限制了其在RGB领域的适用性。同时，训练多光谱多模态模型成本高且产生专门化的模型。为了解决这一问题，我们提出了一种新的免训练方法，将多光谱数据引入标准RGB-only LMMs的推理流程中，从而实现显著的性能提升。我们的方法利用LMMs对视觉空间的理解，通过适应非RGB输入到该空间，并注入领域特定信息和推理链推理作为指令。我们通过Gemini 2.5模型展示了这一方法，并在流行的遥感基准上观察到强大的零样本性能提升。这些结果突显了地理空间专业人员可以利用强大通用模型来处理专门传感器输入的潜力，受益于基于专门数据的丰富推理能力。
英文摘要：
Multi-spectral imagery is a valuable input signal for Remote Sensing applications, such as land-use and land-cover classification and environmental monitoring. However, generalist Large Multi-modal Models (LMMs) are typically trained on RGB images, limiting their applicability to the RGB domain. At the same time, training multi-spectral multi-modal models is expensive and produces uniquely specialized models. To address this, we propose a novel training-free approach that introduces multi-spectral data within the inference pipeline of standard RGB-only LMMs, allowing large gains in performance. Our approach leverages the LMMs' understanding of the visual space by adapting non-RGB inputs to that space and injecting domain-specific information and Chain-of-Thought reasoning as instructions. We demonstrate this with the Gemini 2.5 model and observe strong Zero-Shot performance gains on popular Remote Sensing benchmarks. These results highlight the potential for geospatial professionals to leverage powerful generalist models for specialized sensor inputs, benefiting from rich reasoning capabilities grounded in specialized data.
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。
