重新审视遥感中的变化视觉问答问题：结构化与原生多模态Qwen模型
Revisiting Change VQA in Remote Sensing with Structured and Native Multimodal Qwen Models
AI总结：
本文重新审视遥感中的变化视觉问答问题，比较了结构化视觉语言模型与原生多模态模型在CDVQA基准上的表现，发现原生模型在语义变化推理中更有效。
摘要：
变化视觉问答（Change VQA）旨在回答关于双时制遥感图像之间语义变化的自然语言问题。尽管视觉语言模型（VLMs）最近被研究用于时间遥感图像理解，但Change VQA在现代多模态模型中的应用仍显不足。本文在统一的低秩适应（LoRA）设置下，重新审视CDVQA基准，比较了遵循结构化视觉语言流程的Qwen3-VL与结合单阶段对齐和混合解码器骨干的原生多模态Qwen3.5模型。实验结果表明，近期VLMs在官方CDVQA测试分割上优于早期专用基线。进一步表明性能不随模型规模单调增长，且原生多模态模型比结构化视觉语言流程更有效。这些发现表明，紧密集成的多模态骨干对性能的贡献比规模或显式多深度视觉条件更有效。
英文摘要：
Change visual question answering (Change VQA) addresses the problem of answering natural-language questions about semantic changes between bi-temporal remote sensing (RS) images. Although vision-language models (VLMs) have recently been studied for temporal RS image understanding, Change VQA remains underexplored in the context of modern multimodal models. In this letter, we revisit the CDVQA benchmark using recent Qwen models under a unified low-rank adaptation (LoRA) setting. We compare Qwen3-VL, which follows a structured vision-language pipeline with multi-depth visual conditioning and a full-attention decoder, with Qwen3.5, a native multimodal model that combines a single-stage alignment with a hybrid decoder backbone. Experimental results on the official CDVQA test splits show that recent VLMs improve over earlier specialized baselines. They further show that performance does not scale monotonically with model size, and that native multimodal models are more effective than structured vision-language pipelines for this task. These findings indicate that tightly integrated multimodal backbones contribute more to performance than scale or explicit multi-depth visual conditioning for language-driven semantic change reasoning in RS imagery.
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