Visual Chain-of-Thought Prompting (VCTP) is a novel framework for knowledge-based visual reasoning that enables machines to perform step-by-step reasoning by integrating visual perception and language-based reasoning. The framework consists of three stages: see, think, and confirm. In the see stage, a visual perception model identifies candidate visual concepts in an image. In the think stage, a pre-trained large language model (LLM) attends to key visual concepts from the question and transforms them into text for prompting. The LLM then generates an answer based on the attended visual context. In the confirm stage, the LLM generates a rationale for the answer, which is verified against the visual context using a cross-modality classifier. The think and confirm stages are iteratively repeated to ensure the rationale is consistent with the answer. VCTP has been tested on various knowledge-based visual reasoning datasets and has shown several advantages. It outperforms previous few-shot learning baselines, provides transparent and trustworthy reasoning through rationales, and is computationally efficient compared to other fine-tuning methods. The framework is designed to mimic human reasoning processes, where visual and language-based reasoning are integrated iteratively. The model uses a scene parser to extract visual concepts, an LLM to attend to key concepts, and a captioner to generate text descriptions. The rationale is verified against the visual context to ensure consistency. The framework is implemented with a modular structure, allowing for iterative refinement of the reasoning process. VCTP is effective in answer prediction and provides interpretable reasoning steps, making it suitable for real-world applications such as assistive robots and embodied chatbots. The model is evaluated on standard benchmarks like OK-VQA and A-OKVQA, demonstrating its effectiveness and efficiency in knowledge-based visual reasoning tasks.Visual Chain-of-Thought Prompting (VCTP) is a novel framework for knowledge-based visual reasoning that enables machines to perform step-by-step reasoning by integrating visual perception and language-based reasoning. The framework consists of three stages: see, think, and confirm. In the see stage, a visual perception model identifies candidate visual concepts in an image. In the think stage, a pre-trained large language model (LLM) attends to key visual concepts from the question and transforms them into text for prompting. The LLM then generates an answer based on the attended visual context. In the confirm stage, the LLM generates a rationale for the answer, which is verified against the visual context using a cross-modality classifier. The think and confirm stages are iteratively repeated to ensure the rationale is consistent with the answer. VCTP has been tested on various knowledge-based visual reasoning datasets and has shown several advantages. It outperforms previous few-shot learning baselines, provides transparent and trustworthy reasoning through rationales, and is computationally efficient compared to other fine-tuning methods. The framework is designed to mimic human reasoning processes, where visual and language-based reasoning are integrated iteratively. The model uses a scene parser to extract visual concepts, an LLM to attend to key concepts, and a captioner to generate text descriptions. The rationale is verified against the visual context to ensure consistency. The framework is implemented with a modular structure, allowing for iterative refinement of the reasoning process. VCTP is effective in answer prediction and provides interpretable reasoning steps, making it suitable for real-world applications such as assistive robots and embodied chatbots. The model is evaluated on standard benchmarks like OK-VQA and A-OKVQA, demonstrating its effectiveness and efficiency in knowledge-based visual reasoning tasks.