Remote sensing VLM "More with Less" prioritizes data scale over architecture

Researchers have developed a large-scale remote sensing vision-language model (VLM) called "More with Less" that challenges the need for specialized architectural designs. By training a general-purpose VLM on a diverse dataset and employing a multi-task reinforcement learning framework, the model achieves competitive performance across various remote sensing tasks, including visual question answering, detection, and segmentation. The study suggests that data scale and diversity are more critical for advancing remote sensing VLMs than architectural innovation. AI

IMPACT Suggests that scaling data and diversity is more impactful than architectural novelty for remote sensing VLMs.

RANK_REASON Research paper detailing a new model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• caption
• detection
• Earth observation
• Hugging Face
• More with Less
• multimodality
• Stefan Maria Ailuro
• visual question answering

• paper
• model release

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