Introducing ConTextual: How well can your Multimodal model jointly reason over text and image in text-rich scenes?
Hugging Face Blog
Researchers at UCLA created ConTextual, a benchmark dataset with 506 instructions designed to evaluate how well multimodal AI models can reason jointly about text and images in text-rich scenes like maps, shopping interfaces, and infographics. The dataset covers 8 real-world visual scenarios, and initial experiments tested 13 models including GPT-4V, Gemini Vision Pro, and open-source alternatives like LLaVA-1.5-13B. Current models substantially underperform humans on the benchmark, with even the best proprietary model GPT-4V struggling on time-reading and infographic tasks, suggesting the need for better image encoders and vision-language alignment techniques.
