Vision-Language Models under Cultural and Inclusive Considerations

• URL: http://arxiv.org/abs/2407.06177v1
• Date: Mon, 8 Jul 2024 17:50:00 GMT
• Title: Vision-Language Models under Cultural and Inclusive Considerations
• Authors: Antonia Karamolegkou, Phillip Rust, Yong Cao, Ruixiang Cui, Anders Søgaard, Daniel Hershcovich,
• Abstract summary: Large vision-language models (VLMs) can assist visually impaired people by describing images from their daily lives. Current evaluation datasets may not reflect diverse cultural user backgrounds or the situational context of this use case. We create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate several VLMs, investigating their reliability as visual assistants in a culturally diverse setting.
• Score: 53.614528867159706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large vision-language models (VLMs) can assist visually impaired people by describing images from their daily lives. Current evaluation datasets may not reflect diverse cultural user backgrounds or the situational context of this use case. To address this problem, we create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate several VLMs, investigating their reliability as visual assistants in a culturally diverse setting. While our results for state-of-the-art models are promising, we identify challenges such as hallucination and misalignment of automatic evaluation metrics with human judgment. We make our survey, data, code, and model outputs publicly available.

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