Assessing Color Vision Test in Large Vision-language Models
Abstract
With the widespread adoption of large vision-language models, the capacity for color vision in these models is crucial. However, the color vision abilities of large visual-language models have not yet been thoroughly explored. To address this gap, we define a color vision testing task for large vision-language models and construct a dataset that covers multiple categories of test questions and tasks of varying difficulty levels. Furthermore, we analyze the types of errors made by large vision-language models and propose a chain-of-thought prompting strategy to enhance their performance in color vision tests.
Fields of Study & Subjects
Categorization
perception
and language
Publication details
- Journal / source
- Proceedings of the 2026 International Conference on Multimedia Retrieval
- Publisher
- ACM
- Pages
- 2714–2722
- Publication date
- 15 June 2026
- Language
- EN