World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models
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.
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Eunsu Kim
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2025-12-01T10:17:29.036Z
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Eunsu Kim
EunsuKim
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EunsuKim
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Junyeong Park
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Na Min An
claimed_verified
2025-12-02T08:49:44.579Z
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Na Min An
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user
namin0202
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Junseong Kim
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Hitesh Laxmichand Patel
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Jiho Jin
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2025-12-01T10:48:30.428Z
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Jiho Jin
jinjh0123
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jinjh0123
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Julia Kruk
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Amit Agarwal
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Srikant Panda
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Fenal Ashokbhai Ilasariya
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Hyunjung Shim
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Alice Oh
admin_assigned
2025-12-01T10:49:03.130Z
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Alice Oh
aliceoh
user
aliceoh
World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models
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.
LVLMs struggle with preserving cultural identities in mixed visual scenes, but supervised fine-tuning with culture mixing datasets improves their performance.
Large Vision-Language Models LVLMs Visual Question Answering VQA diffusion-generated culture mixing food-only food+food food+background food+food+background supervised fine-tuning