Summary of Exploring Camera Encoder Designs For Autonomous Driving Perception, by Barath Lakshmanan et al.
Exploring Camera Encoder Designs for Autonomous Driving Perception
by Barath Lakshmanan, Joshua Chen, Shiyi Lan, Maying Shen, Zhiding Yu, Jose M. Alvarez
First submitted to arxiv on: 9 Jul 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 paper proposes a novel approach to designing camera encoders for autonomous vehicles (AVs). Existing architectures, such as Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs), are modified to better suit the requirements of industrial-level AV datasets. The authors start with a standard general-purpose encoder, ConvNeXt, and progressively transform its design by adjusting various parameters like width and depth, stage compute ratio, attention mechanisms, and input resolution. This customization yields an optimized architecture that achieves 8.79% mAP improvement over the baseline for AV camera encoding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps improve autonomous vehicles’ perception systems by customizing camera encoders. It starts with a general-purpose model and adjusts its design to better suit industrial-level datasets. This results in a more accurate encoder, achieving an 8.79% boost. |
Keywords
» Artificial intelligence » Attention » Encoder
