Gender and Racial Bias in Visual Question Answering Datasets. (arXiv:2205.08148v2 [cs.CV] UPDATED)

Vision-and-language tasks have increasingly drawn more attention as a means
to evaluate human-like reasoning in machine learning models. A popular task in
the field is visual question answering (VQA), which aims to answer questions
about images. However, VQA models have been shown to exploit language bias by
learning the statistical correlations between questions and answers without
looking into the image content: e.g., questions about the color of a banana are
answered with yellow, even if the banana in the image …

arxiv bias cv datasets gender question answering