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Achieving robust hyperspectral image classification without access to source data could revolutionize remote sensing applications constrained by privacy regulations.
Today's best multimodal LLMs are surprisingly inept at using tools to solve agricultural tasks, struggling with everything from planning to error recovery.
VLMs forget visual reasoning skills in continual learning because today's methods over-protect the language model while neglecting the vision encoder.
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.
