A Unified Framework for Efficient Remote Sensing Visual Question Answering: Adapting Dual, Hybrid, and Encoder-Decoder Architectures
Researchers have developed a unified framework called RS Adapter, a Parameter Efficient Fine Tuning (PEFT) strategy, to adapt existing Vision Language Models (VLMs) for Remote Sensing Visual Question Answering (RSVQA). This method injects lightweight adapters into three distinct VLM architectures: Dual Encoder CLIP, Encoder Decoder BLIP, and Hybrid FLAVA. Experiments on the RSVQA-x dataset show that while all adapted models converge, the Hybrid FLAVA architecture provides the best balance of reasoning and retrieval capabilities, establishing a new baseline for efficient VQA in applications like disaster assessment and urban monitoring. AI
IMPACT This research offers a more resource-efficient method for applying advanced vision-language models to specialized domains like remote sensing, potentially accelerating applications in disaster assessment and urban monitoring.