Summary of Cross-domain Few-shot In-context Learning For Enhancing Traffic Sign Recognition, by Yaozong Gan et al.
Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition
by Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 cross-domain few-shot in-context learning method based on multimodal large language models (MLLM) improves traffic sign recognition (TSR) by generating description texts for fine-grained traffic sign categories. This approach reduces the dependence on training data and enhances the MLLM’s perception of fine-grained traffic sign categories. The method consists of a traffic sign detection network using Vision Transformer Adapter and an extraction module to extract traffic signs from original road images. To evaluate its performance, the proposed method is tested on several datasets, including the German traffic sign recognition benchmark dataset, the Belgium traffic sign dataset, and two real-world datasets taken from Japan. The results show that our method significantly enhances TSR performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to recognize traffic signs using big language models. These models are good at understanding text, but not so good at recognizing images. To solve this problem, the authors come up with an idea: generate descriptions of traffic signs in simple text. This helps the model learn what different traffic signs look like and how they’re different from each other. The method is tested on several real-world datasets and shows that it can significantly improve traffic sign recognition. |
Keywords
» Artificial intelligence » Few shot » Vision transformer
