Summary of Image-of-thought Prompting For Visual Reasoning Refinement in Multimodal Large Language Models, by Qiji Zhou et al.
Image-of-Thought Prompting for Visual Reasoning Refinement in Multimodal Large Language Models
by Qiji Zhou, Ruochen Zhou, Zike Hu, Panzhong Lu, Siyang Gao, Yue Zhang
First submitted to arxiv on: 22 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Image-of-Thought (IoT) prompting method enhances Multimodal Large Language Models’ (MLLMs) ability to tackle complex multimodal reasoning problems. By automatically designing critical visual information extraction operations based on input images and questions, IoT prompts MLLMs to extract step-by-step visual rationales that support answers to complex visual reasoning questions. This approach not only improves zero-shot visual reasoning performance across various tasks but also provides step-by-step visual feature explanations, elucidating the visual reasoning process and aiding in analyzing the cognitive processes of large multimodal models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are getting better at solving complex problems. To make them even better, researchers have created a new way to help these models understand images. This method is called Image-of-Thought (IoT). IoT helps the model figure out what’s important in an image and why it matters for answering questions about that image. The more we can improve this process, the better computers will be at understanding complex things like pictures. |
Keywords
» Artificial intelligence » Prompting » Zero shot
