  Recent advancements in Chain-of-Thought (CoT) and related rationale-based
works have significantly improved the performance of Large Language Models
(LLMs) in complex reasoning tasks. With the evolution of Multimodal Large
Language Models (MLLMs), enhancing their capability to tackle complex
multimodal reasoning problems is a crucial frontier. However, incorporating
multimodal rationales in CoT has yet to be thoroughly investigated. We propose
the Image-of-Thought (IoT) prompting method, which helps MLLMs to extract
visual rationales step-by-step. Specifically, IoT prompting can automatically
design critical visual information extraction operations based on the input
images and questions. Each step of visual information refinement identifies
specific visual rationales that support answers to complex visual reasoning
questions. Beyond the textual CoT, IoT simultaneously utilizes visual and
textual rationales to help MLLMs understand complex multimodal information. IoT
prompting has improved zero-shot visual reasoning performance across various
visual understanding tasks in different MLLMs. Moreover, the step-by-step
visual feature explanations generated by IoT prompting elucidate the visual
reasoning process, aiding in analyzing the cognitive processes of large
multimodal models
