World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models
Authors: name: Eunsu Kim; name: Junyeong Park; name: Na Min An; name: Junseong Kim; name: Hitesh Laxmichand Patel; name: Jiho Jin; name: Julia Kruk; name: Amit Agarwal; name: Srikant Panda; name: Fenal Ashokbhai Ilasariya; name: Hyunjung Shim; name: Alice Oh
Published: 2025-11-27 22:23:08

Abstract: In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large Vision-Language Models (LVLMs) perceive them remains underexplored. We investigate culture mixing as a critical challenge for LVLMs and examine how current models behave when cultural items from multiple regions appear together. To systematically analyze these behaviors, we construct CultureMix, a food Visual Question Answering (VQA) benchmark with 23k diffusion-generated, human-verified culture mixing images across four subtasks: (1) food-only, (2) food+food, (3) food+background, and (4) food+food+background. Evaluating 10 LVLMs, we find consistent failures to preserve individual cultural identities in mixed settings. Models show strong background reliance, with accuracy dropping 14% when cultural backgrounds are added to food-only baselines, and they produce inconsistent predictions for identical foods across different contexts. To address these limitations, we explore three robustness strategies. We find supervised fine-tuning using a diverse culture mixing dataset substantially improve model consistency and reduce background sensitivity. We call for increased attention to culture mixing scenarios as a critical step toward developing LVLMs capable of operating reliably in culturally diverse real-world environments.