  The evaluation of text-generative vision-language models is a challenging yet
crucial endeavor. By addressing the limitations of existing Visual Question
Answering (VQA) benchmarks and proposing innovative evaluation methodologies,
our research seeks to advance our understanding of these models' capabilities.
We propose a novel VQA benchmark based on well-known visual classification
datasets which allows a granular evaluation of text-generative vision-language
models and their comparison with discriminative vision-language models. To
improve the assessment of coarse answers on fine-grained classification tasks,
we suggest using the semantic hierarchy of the label space to ask automatically
generated follow-up questions about the ground-truth category. Finally, we
compare traditional NLP and LLM-based metrics for the problem of evaluating
model predictions given ground-truth answers. We perform a human evaluation
study upon which we base our decision on the final metric. We apply our
benchmark to a suite of vision-language models and show a detailed comparison
of their abilities on object, action, and attribute classification. Our
contributions aim to lay the foundation for more precise and meaningful
assessments, facilitating targeted progress in the exciting field of
vision-language modeling.
