1. FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture
- Author
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Li, Wenyan, Zhang, Xinyu, Li, Jiaang, Peng, Qiwei, Tang, Raphael, Zhou, Li, Zhang, Weijia, Hu, Guimin, Yuan, Yifei, Søgaard, Anders, Hershcovich, Daniel, and Elliott, Desmond
- Subjects
Computer Science - Computation and Language - Abstract
Food is a rich and varied dimension of cultural heritage, crucial to both individuals and social groups. To bridge the gap in the literature on the often-overlooked regional diversity in this domain, we introduce FoodieQA, a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China. We evaluate vision-language Models (VLMs) and large language models (LLMs) on newly collected, unseen food images and corresponding questions. FoodieQA comprises three multiple-choice question-answering tasks where models need to answer questions based on multiple images, a single image, and text-only descriptions, respectively. While LLMs excel at text-based question answering, surpassing human accuracy, the open-sourced VLMs still fall short by 41\% on multi-image and 21\% on single-image VQA tasks, although closed-weights models perform closer to human levels (within 10\%). Our findings highlight that understanding food and its cultural implications remains a challenging and under-explored direction.
- Published
- 2024