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Southwest Jiaotong University, Agricultural University
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Today's best multimodal LLMs are surprisingly inept at using tools to solve agricultural tasks, struggling with everything from planning to error recovery.
Extracting agricultural parcels from satellite imagery gets a whole lot harder (and more realistic) with a new dataset focused on the complex, irregular, and heterogeneous terrain of terraced farms.
Achieve state-of-the-art performance in multimodal remote sensing semantic segmentation with significantly fewer trainable parameters by using a novel parameter-efficient and modality-balanced symmetric fusion framework.
