  Vision-Language Pretraining (VLP) models have recently successfully
facilitated many cross-modal downstream tasks. Most existing works evaluated
their systems by comparing the fine-tuned downstream task performance. However,
only average downstream task accuracy provides little information about the
pros and cons of each VLP method, let alone provides insights on how the
community can improve the systems in the future. Inspired by the CheckList for
testing natural language processing, we exploit VL-CheckList, a novel framework
to understand the capabilities of VLP models. The proposed method divides the
image-texting ability of a VLP model into three categories: objects,
attributes, and relations, and uses a novel taxonomy to further break down
these three aspects. We conduct comprehensive studies to analyze seven recently
popular VLP models via the proposed framework. Results confirm the
effectiveness of the proposed method by revealing fine-grained differences
among the compared models that were not visible from downstream task-only
evaluation. Further results show promising research direction in building
better VLP models. Our data and code are available at:
https://github.com/om-ai-lab/VL-CheckList.
