  Compositional reasoning capabilities are usually considered as fundamental
skills to characterize human perception. Recent studies show that current
Vision Language Models (VLMs) surprisingly lack sufficient knowledge with
respect to such capabilities. To this end, we propose to thoroughly diagnose
the composition representations encoded by VLMs, systematically revealing the
potential cause for this weakness. Specifically, we propose evaluation methods
from a novel game-theoretic view to assess the vulnerability of VLMs on
different aspects of compositional understanding, e.g., relations and
attributes. Extensive experimental results demonstrate and validate several
insights to understand the incapabilities of VLMs on compositional reasoning,
which provide useful and reliable guidance for future studies. The deliverables
will be updated at https://vlms-compositionality-gametheory.github.io/.
