Yue Zhou, Shujun Zhao, Xue Yang, Ruigang Li, Tianwen Zhang, Mengcheng Lan, Chaofeng Chen, Lingfei Ma · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31304 · open-access PDF
Durante a era do deep learning, as inovações em modelos de visão de sensores remotos (RS) focaram-se principalmente na otimização de arquiteturas de rede para tarefas específicas e na realização de treinamento de ponta a ponta. No entanto, com o advento da era dos modelos de linguagem de visão (VLMs), constrangidos por exigências substanciais de recursos computacionais, o paradigma de pesquisa mainstream mudou para adaptar VLMs de propósito geral usando dados de domínio vertical RS para ajustes finos, em vez de modificar suas arquiteturas centrais.
Machine translation (Meta M2M100); the English original follows.
During the deep learning era, innovations in remote sensing (RS) vision models primarily focused on optimizing network architectures for specific tasks and conducting end-to-end training. However, with the advent of the Vision-Language Models (VLMs) era, constrained by substantial computational resource demands, the mainstream research paradigm has shifted towards adapting general-purpose VLMs using RS vertical domain data for fine-tuning, rather than modifying their core architectures. This transition marks a change in the innovation pathway from model-centric to data-centric. Despite the inc
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