遥感持续视觉-语言学习：基准测试与分析
Continual Vision-Language Learning for Remote Sensing: Benchmarking and Analysis
AI总结：
本文提出CLEaRS基准，评估遥感视觉-语言模型的持续学习能力，发现现有模型存在灾难性遗忘，需开发专用持续学习方法。
摘要：
当前遥感视觉-语言模型（RS VLMs）在图像解释中表现优异，但依赖静态训练数据，限制了其适应持续新兴传感模态和下游任务的能力。本文提出CLEaRS基准，包含10个定制子集和207k+图像-文本对，定义长周期、模态增量和任务增量评估协议，发现所有设置下均存在灾难性遗忘，现有持续学习方法在处理任务、指令和模态转换时效果有限。
英文摘要：
Current remote sensing vision-language models (RS VLMs) demonstrate impressive performance in image interpretation but rely on static training data, limiting their ability to accommodate continuously emerging sensing modalities and downstream tasks. This exposes a fundamental challenge: enabling RS VLMs to continually adapt without catastrophic forgetting. Despite its practical importance, the continual learning capability of RS VLMs remains underexplored, and no dedicated benchmark currently exists. In this work, we present CLeaRS, a comprehensive benchmark for continual vision-language learning in remote sensing. CLeaRS comprises 10 curated subsets with over 207k image-text pairs, spanning diverse interpretation tasks, sensing modalities, and application scenarios. We further define three evaluation protocols: long-horizon, modality-incremental, and task-incremental settings, to systematically assess continual adaptation. Extensive benchmarking of diverse vision-language models reveals catastrophic forgetting across all settings. Moreover, representative continual learning methods, when adapted to RS VLMs, exhibit limited effectiveness in handling task, instruction, and modality transitions. Our findings underscore the need for developing continual learning methods tailored to RS VLMs.
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。
