Summary of A Survey Of Vision Transformers in Autonomous Driving: Current Trends and Future Directions, by Quoc-vinh Lai-dang
A Survey of Vision Transformers in Autonomous Driving: Current Trends and Future Directions
by Quoc-Vinh Lai-Dang
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper surveys the adoption of visual transformer models in Autonomous Driving, a trend inspired by their success in Natural Language Processing. Visual transformers have been shown to surpass traditional Recurrent Neural Networks in tasks like sequential image processing and outperform Convolutional Neural Networks in global context capture, as seen in complex scene recognition. These capabilities are crucial for real-time, dynamic visual scene processing in Autonomous Driving. The survey provides a comprehensive overview of Vision Transformer applications in Autonomous Driving, covering foundational concepts such as self-attention, multi-head attention, and encoder-decoder architecture. Applications include object detection, segmentation, pedestrian detection, lane detection, and more. This paper also compares the architectural merits and limitations of these models and concludes with future research directions, highlighting the growing role of Vision Transformers in Autonomous Driving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how a type of AI called visual transformers is being used to help self-driving cars understand what’s going on around them. Visual transformers are good at processing images and recognizing patterns, which is important for self-driving cars because they need to be able to quickly see and respond to their surroundings. The paper shows that these models can do better than other types of AI in certain tasks, like recognizing complex scenes or detecting objects. It also looks at how these models are being used in different applications, such as object detection and lane detection. Overall, the paper is about how visual transformers are becoming more important for self-driving cars. |
Keywords
* Artificial intelligence * Encoder decoder * Multi head attention * Natural language processing * Object detection * Self attention * Transformer * Vision transformer
