Pix2Fact: When Vision Is Not Enough -- Benchmarking Fine-Grained VQA with Web Verification on High-Resolution Real-World Scenes
arXiv:2602.00593v3 Announce Type: replace-cross Abstract: Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and external knowledge, a synergy overlooked by existing benchmarks that evaluate these abilities in isolation. To fill this void, we introduce Pix2Fact, a visual question-answering benchmark designed to assess expert-level visual perception and knowledge search. Pix2Fact comprises 1,000 high-resolution (4K+) images spanning eight scenarios. Its questions and answers are meticulously crafted by P
The continuous evolution of vision-language models necessitates more sophisticated benchmarks to push their capabilities beyond general tasks, particularly in fine-grained analysis and external knowledge integration.
This benchmark highlights a critical gap in current AI capabilities for advanced perception and knowledge retrieval, signaling areas for concentrated research and development to achieve truly expert-level AI.
The introduction of Pix2Fact provides a standardized and challenging evaluation method for vision-language models, which will drive focused innovation towards more human-like visual reasoning and factual grounding.
- · AI research labs
- · Developers of fine-grained VLM
- · Industries requiring high-precision visual analysis
- · VLMs lacking robust external knowledge integration
- · Benchmarks focusing only on isolated VQA abilities
VLMs will be forced to develop more robust mechanisms for fine-grained visual grounding and external knowledge retrieval to perform well on new benchmarks.
Improved VLM performance could lead to advancements in complex applications like autonomous inspection, medical diagnostics, and enhanced content creation tools.
The pursuit of expert-level visual perception and knowledge search could accelerate the development of more general artificial intelligence capabilities.
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Read at arXiv cs.LG