WikiVQABench: A Knowledge-Grounded Visual Question Answering Benchmark from Wikipedia and Wikidata
arXiv:2605.21479v1 Announce Type: cross Abstract: Visual Question Answering (VQA) benchmarks have largely emphasized perception-based tasks that can be solved from visual content alone. In contrast, many real-world scenarios require external knowledge that is not directly observable in the image to answer correctly. We introduce WikiVQABench, a human-curated knowledge-grounded VQA benchmark constructed by systematically combining Wikipedia images, their associated article captions, and structured knowledge from Wikidata. Our pipeline uses large language models (LLMs) to generate candidate mult
The proliferation of advanced LLMs and the recognition of VQA limitations highlight the need for benchmarks that integrate external knowledge, driving development in this area.
This benchmark addresses a critical gap in VQA, shifting focus from purely perceptual tasks to those requiring complex reasoning and external knowledge, a key step towards more capable AI systems.
VQA model development will now have a robust benchmark for evaluating knowledge-grounded capabilities, leading to more practical and accurate AI applications beyond visual recognition.
- · AI researchers
- · Developers of knowledge graphs
- · Generative AI companies
- · Academic institutions
- · VQA models reliant solely on visual cues
- · Benchmarks focusing only on perception
- · AI applications lacking external knowledge integration
Further development of VQA models that can effectively leverage external knowledge bases.
Increased integration of knowledge graphs and large language models for more robust AI reasoning across various domains.
Acceleration of AI agent development that can autonomously acquire and apply information from diverse sources, mirroring human cognitive processes.
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Read at arXiv cs.AI