SnapNTell enhances Visual Question Answering (VQA) by tackling long-tail entities and providing detailed, entity-specific knowledge. It introduces a distinctive dataset and a scalable multimodal LLM approach. Key features include:

• Entity-Centric Benchmark: Designed to evaluate entities recognition and in-depth knowledge.
• SnapNTell Dataset: Comprises 7,568 unique entities, each with 10 images and knowledge-intensive Q&A.
• Improved Performance: Achieves notable enhancement in BELURT score by 66.5%.
• Public Resources: Dataset and source code to be made available.

SnapNTell represents significant progress in the VQA domain Read more.

This endeavor is paramount as it adds a new layer of sophistication to VQA systems. By focusing on entity-centric questions, it opens doors for more accurate and information-rich responses, vital for domains like medical imaging and autonomous navigation.