arxiv preprint – DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM

In this episode, we discuss DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM by Yixuan Wu, Yizhou Wang, Shixiang Tang, Wenhao Wu, Tong He, Wanli Ouyang, Philip Torr, Jian Wu. DetToolChain introduces a prompting toolkit and a Chain-of-Thought methodology to enhance zero-shot object detection capabilities in multimodal large language models like GPT-4V and Gemini. The toolkit employs precise detection strategies and tools such as zooming, overlaying rulers, and scene graphs to help the models focus and infer better. Experimental results demonstrate significant performance improvements in various detection tasks, surpassing state-of-the-art methods considerably.