arXiv 2606.282662026-06-29cs.CV

RSICCLLM：用于遥感图像变化描述的多模态大语言模型

RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning

State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China（人工智能安全国家重点实验室，计算技术研究所，中国科学院，北京，中国）；Great Bay University, Dongguan, China（东莞Great Bay大学，中国）；Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳学院，中国）；Dongguan Key Laboratory for Intelligence and Information Technology, Dongguan, China（东莞智能与信息科技重点实验室，中国）

Yelin Wang, Zijia Song, Shuo Ye, Chuanguang Yang, Miaoyu Wang, Yong Xu, Zhulin An, Yongjun Xu, Zitong Yu

备注：: Accepted by ECCV 2026

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AI总结：

提出首个针对遥感图像变化描述任务的大视觉语言模型后训练框架RSICCLLM，通过数据生成范式、差异感知微调和双负偏好优化，仅7B参数即超越更大规模模型。

摘要：

遥感图像变化描述（RSICC）旨在描述双时相遥感图像之间的变化，具有重要的研究和应用价值。然而，现有方法大多依赖传统深度学习架构，有限的模型容量制约了性能。尽管大模型后训练技术在通用领域取得了巨大成功，但由于数据稀缺和细粒度变化理解的需求，直接迁移到RSICC仍面临挑战。为此，我们提出了RSICCLLM，这是首个针对RSICC的大视觉语言模型后训练框架。具体而言，我们设计了一种数据生成范式，发布了指令数据集RSICI，并建立了任务特定的RSICC基准。我们进一步引入了差异感知监督微调，以显式提取变化表示并引导模型感知和理解时间差异。此外，我们提出了双负偏好优化（DNPO），采用两种互补的负样本构建策略来构建偏好数据集RSICP，并进一步优化模型性能。大量实验验证了RSICCLLM的卓越能力，仅用7B参数就取得了显著成果，超越了更大规模的模型。代码和数据集将在https://this URL公开。

英文摘要：

Remote Sensing Image Change Captioning (RSICC) aims to describe changes between bi-temporal remote sensing images and holds significant research and application value. However, most existing methods rely on conventional deep learning architectures, and the limited model capacity constrains performance. Although large-model post-training techniques have achieved great success in general domains, their direct transfer to RSICC remains challenging due to data scarcity and the need for fine-grained change understanding. To address this, we propose RSICCLLM, the first post-training framework for large vision-language models in RSICC. Specifically, we design a data generation paradigm, release the instruction dataset RSICI, and establish a task-specific RSICC benchmark. We further introduce Difference-aware Supervised Fine-tuning to explicitly extract change representations and guide the model in perceiving and understanding temporal differences. In addition, we propose Dual-Negative Preference Optimization (DNPO), which employs two complementary negative-sample construction strategies to construct the preference dataset RSICP and further refine model performance. Extensive experiments validate the superior capability of RSICCLLM, which achieves outstanding results with only 7B parameters, surpassing models of substantially larger scales. The code and dataset will be made publicly available at https://github.com/keaill/RSICCLLM.

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。