Summary of Dettoolchain: a New Prompting Paradigm to Unleash Detection Ability Of Mllm, by Yixuan Wu et al.
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
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 DetToolChain paradigm enables multimodal large language models (MLLMs) to perform zero-shot object detection tasks without requiring explicit training data. The toolkit comprises prompts inspired by high-precision detection priors, which guide the MLLM to focus on regional information, read coordinates, and infer from contextual information. This framework can automatically decompose detection tasks into subtasks, diagnose predictions, and plan for progressive box refinements. By leveraging this framework, GPT-4V achieves state-of-the-art performance on various object detection benchmarks, including MS COCO Novel class set (+21.5% AP50), RefCOCO val set (+24.23% Acc), and D-cube describe object detection FULL setting (+14.5% AP). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research presents a new way to help large language models find objects in images without needing special training for each object type. This is done by giving the model specific instructions on how to look at an image, like focusing on certain parts or reading coordinates. The model can then use these instructions to make more accurate predictions about what’s in the image. This approach allows the model to perform well even when it hasn’t seen the objects before. |
Keywords
» Artificial intelligence » Gpt » Object detection » Precision » Zero shot
