  Compositional visual reasoning methods, which translate a complex query into
a structured composition of feasible visual tasks, have exhibited a strong
potential in complicated multi-modal tasks. Empowered by recent advances in
large language models (LLMs), this multi-modal challenge has been brought to a
new stage by treating LLMs as few-shot/zero-shot planners, i.e.,
vision-language (VL) programming. Such methods, despite their numerous merits,
suffer from challenges due to LLM planning mistakes or inaccuracy of visual
execution modules, lagging behind the non-compositional models. In this work,
we devise a "plug-and-play" method, ExoViP, to correct errors in both the
planning and execution stages through introspective verification. We employ
verification modules as "exoskeletons" to enhance current VL programming
schemes. Specifically, our proposed verification module utilizes a mixture of
three sub-verifiers to validate predictions after each reasoning step,
subsequently calibrating the visual module predictions and refining the
reasoning trace planned by LLMs. Experimental results on two representative VL
programming methods showcase consistent improvements on five compositional
reasoning tasks on standard benchmarks. In light of this, we believe that
ExoViP can foster better performance and generalization on open-domain
multi-modal challenges.
