Assessing Color Vision Test in Large Vision-language Models
This work addresses a specific gap in evaluating color vision for users of large vision-language models, but it appears incremental as it focuses on testing and improvement strategies without introducing a new paradigm.
The paper tackles the problem of assessing color vision abilities in large vision-language models, which had not been thoroughly explored, by defining a testing task and constructing a dataset with multiple categories and difficulty levels; the result includes analyzing error types and proposing fine-tuning strategies to enhance performance, though no concrete numbers are provided.
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 \footnote{Anonymous Github Showing some of the data https://anonymous.4open.science/r/color-vision-test-dataset-3BCD} 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 fine-tuning strategies to enhance their performance in color vision tests.