  Language-Image Pre-training has demonstrated promising results on zero-shot
and few-shot downstream tasks by prompting visual models with natural language
prompts. However, most recent studies only use a single prompt for tuning,
neglecting the inherent step-to-step cognitive reasoning process that humans
conduct in complex task settings, for example, when processing images from
unfamiliar domains. Chain of Thought is a simple and effective approximation to
human reasoning process and has been proven useful for natural language
processing (NLP) tasks. Based on this cognitive intuition, we believe that
conducting effective reasoning is also an important problem in visual tasks,
and a chain of thought could be a solution to this problem. In this work, we
propose a novel chain of thought prompt tuning for vision-language modeling.
Extensive experiments show that our method not only generalizes better in image
classification tasks, has greater transferability beyond a single dataset, and
has stronger domain generalization performance, but also performs much better
in imagetext retrieval and visual question answering, which require more
reasoning capabilities. We are the first to successfully adapt chain-of-thought
prompting that combines visual and textual embeddings. We will release our
codes
