New RSJEV framework uses MLLMs for efficient remote sensing scene classification

Researchers have introduced RSJEV, a novel framework for remote sensing scene classification that utilizes multimodal large language models (MLLMs). Unlike traditional MLLMs that generate text, RSJEV reformulates classification as a discriminative decision process, directly estimating category probabilities without autoregressive decoding. This approach, tested on benchmarks like UC Merced and AID, shows improved performance over existing CNN, Transformer, Mamba, and CLIP-based methods, while also reducing inference costs with a smaller model. AI

IMPACT This new framework could lead to more efficient and accurate analysis of satellite imagery for various geospatial applications.

RANK_REASON The item is a research paper detailing a new method for remote sensing scene classification using multimodal large language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Aid
• CNN
• Mamba
• multimodal large language model
• NWPU-RESISC45
• RSJEV
• transformer
• University of California, Merced

• paper
• model release

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