Efficient Knowledge-based Visual Question Answering by Learning to Compress and Aggregate Contextual Information

RACC, a framework that learns to compress and aggregate retrieved contexts, achieves state-of-the-art performance on knowledge-based visual question answering tasks while significantly reducing inference latency and storage requirements.