Vision-Language Models under Cultural and Inclusive Considerations (2024)

Abstract: Large Vision Language Models can be used to assist visually impaired individuals by describing images they capture in their daily lives. Current evaluation datasets may not reflect the diverse cultural user backgrounds nor 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 different models and prompts, investigating their reliability as visual assistants. While the evaluation results for state-of-the-art models seem promising, we identified some weak spots such as hallucinations and problems with conventional evaluation metrics. Our survey, data, code, and model outputs will be publicly available.

## introduction with the increasing integration of ai applications into our lives, it is important to consider humancentered use cases when evaluating such systems. large multimodal language models are now used as visual assistants for blind and visually impaired individuals. given that people across different cultures use such applications, it is essential to ensure not only their accuracy and faithfulness @xcite but also their cultural representation and inclusion @xcite . existing evaluation benchmarks for vlms focus primarily on english with few, implicit mutlicultural references. although multicultural evaluation datasets like marvl @xcite and xm3600 @xcite include culturespecific images (e.g., traditional wedding costumes), they also contain images with minimal cultural significance (e.g., a bag of carrots). consequently, these datasets may not accurately measure the cultural knowledge of vlms, despite being useful for assessing their multilingual capabilities. additionally, evaluating these systems as visual assistants presents further challenges due to varying photo quality, user goals, and photo content @xcite . recently, @xcite conducted a diary study with blind and low-vision individuals using an ai-powered scene description application, revealing that significant improvements are still needed for satisfying and trustworthy user experiences. to address both cultural and visual challenges, we first surveyed visually impaired individuals to gather their caption preferences and determine if cultural details are necessary. then, we filtered an existing dataset with images taken from people who are blind, identifying implicit cultural concepts. this is used as a challenging benchmark to evaluate image captioning performance on cultural images of state-of-the-art models across different prompt settings. with these experiments, we investigate how ai applications, such as image captioning, can foster a more inclusive and culture-aware experience for all. background current models are trained without consideration for the subjective perspectives and cultural influences of those who provided the image descriptions @xcite . this raises the need for carefully curated sources of data and annotation paradigms that are more culturally aware and inclusive @xcite . lately, there has been a growing body of work releasing multicultural multimodal datasets for visiolinguistic reasoning @xcite ), text to image generation @xcite , and image captioning @xcite . beyond the focus on the multilingualism of the captions, concurrent work also addresses the cultural concepts depicted in the images @xcite @xcite @xcite . however, they still do not take into account specific use cases, such as visual assistance. @xcite released the first image-captioning dataset with photos from people who are blind, and a series of challenges for multimodal systems across different tasks @xcite . after this initiative, there have been many works trying to improve current models for a specific use-case, to assist people with visual disabilities @xcite @xcite . there has also been research in human-computer interaction (hci) and accessibility on designing image descriptions for visually impaired individuals, primarily focusing on screen readers and functional descriptions of online, publicly available images @xcite @xcite . despite these efforts, there still seems to be a lack of focus on image captioning for the visually impaired @xcite , especially in multi-cultural settings. ## methodology we first created a survey seeking to understand the preferences of visually impaired individuals for image captions, focusing on the inclusion of cultural information and the desired level of detail (see appendix a). we aggregate the participants' assessments of the helpfulness and importance of cultural information in figure 1 . we then focused on two lines of contribution: (1) we filtered the vizwiz dataset for implicit cultural concepts. vizwiz is a widely used visual question answering and image captioning dataset representing a real-world use case, where examples consist of images and questions submitted by people who are blind, together with crowdsourced answers and image captions @xcite . the selection of this dataset serves two main purposes. firstly, it is a challenging dataset specifically tailored to realworld challenges faced by people seeking to access visual information. secondly, vizwiz might contain implicit cultural references that are currently not captured due to the lack of culture-specific captions. (2) we evaluated the image captioning performance of state-of-the-art close-sourced and opensourced models in a culturally diverse setting using our filtered vizwiz dataset. we performed both an automatic scoring of model-generated captions against two sets of annotations using the coco evaluation package foot_0 and a human evaluation. ## further analysis to further analyze our results and assess the modelgenerated captions in a more fine-grained manner, we manually inspected all generated captions for our 324 images filtered vizwiz dataset and provided some examples in figure 2 . we find that instructblip and blip-2 captions tend to be very short, lack a lot of information, and are often irrelevant hallucinations. this is, to an extent, expected as we perform zero-shot captioning, so the models are not necessarily accustomed to the desired captioning style. in this case, few-shot prompting or finetuning the models would likely improve model performance @xcite @xcite . the closedaccess models, in contrast, largely provide further or more useful and culture-specific details about the image than given by the human captioners. they also seem to provide more accurate captions compared to the open-sourced models. these points may explain why gpt-4o and gemini-1.5-pro and were overall preferred in our human evaluation. overall, we observed that the closed-access models can transcribe various language scripts from books, food or beverage packages, giving them an advantage over the smaller models. in most cases, in both culture-specific and default prompts, the models can identify culture-specific beverages like japanese matcha tea, chinese jelly grass or lychee juice, and food such as the indian lijjat papad, japanese mochi, tom kha gai thai soup, korean kimchi, etc. there are also cases where they identify religious or folk items like the wayang golek puppets, a jar with traditional norwegian costumes, or a delft plaque with traditional dutch costumes. there is, however, a tendency to generate longer text in the culture-specific prompts by adding generic phrases such as 'hinting at the drink's cultural origin', 'suggesting a celebration of their shared heritage', 'highlighting its appeal across age groups in indian culture', etc. the most challenging cases for the closed-source models seem to be foreign currencies (especially the arabic ones), historic figures, and paintings. for example, models seem to confuse bahraini, jordan, and egyptian banknotes, and they do not recognize the chinese historical figure of sun yat-sen, or paintings of joan miró or frederick morgan. we provide further examples in appendix g. ## discussion given the current integration of vlms as virtual assistants for people who seek sighted support, their performance on culture-specific image captioning seems promising. examples from our error analysis and case studies highlight some remaining challenges. measured by automatic evaluation metrics, the performance of the models is overall relatively low compared to results in existing studies evaluating (finetuned) vlms for image captioning on the full vizwiz and other datasets @xcite @xcite . on the other hand, our human evaluation and error analysis show that the generated captions by gemini-1.5-pro and gpt-4o are accurate and preferred in many cases. there also seems to be an extended hallucination problem, which remains an existing major challenge not only for vlms @xcite but across various language model applications @xcite . ## conclusion we evaluated the cultural performance of various models on image captioning using a multicultural dataset tailored to a real-world use case. although the performance of state-of-the-art closed-source models is promising, there is plenty room for improvement. examples from our error analysis provide insights into the models' performance, helping us identify some of their weak spots. in our use case, we find that automatic evaluation metrics might not be fully representative of model performance, and therefore encourage researchers to reconsider a more comprehensive assessment framework. for future work, we aim to extend our small filtered cultural dataset by including questionanswering tasks with pov cultural questions.