Summary of Learning Content-aware Multi-modal Joint Input Pruning Via Bird’s-eye-view Representation, by Yuxin Li et al.
Learning Content-Aware Multi-Modal Joint Input Pruning via Bird’s-Eye-View Representation
by Yuxin Li, Yiheng Li, Xulei Yang, Mengying Yu, Zihang Huang, Xiaojun Wu, Chai Kiat Yeo
First submitted to arxiv on: 9 Oct 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 introduces a novel content-aware multi-modal joint input pruning technique for autonomous driving, which leverages Bird’s-Eye-View (BEV) representation as a shared anchor. The BEV paradigm shifts the sensor fusion challenge from rule-based to data-centric, facilitating nuanced feature extraction. However, high-capacity hardware is often required, making practical implementation challenging. To mitigate this limitation, the authors propose a pruning technique that identifies and eliminates non-essential sensor regions prior to introduction into the perception model’s backbone. Experimental results on the NuScenes dataset demonstrate substantial computational efficiency without sacrificing accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving needs better ways to combine information from different sensors. The “Bird’s-Eye-View” method is a good approach, but it can be too computationally expensive for real-world use. This paper presents a new way to make the BEV method more practical by removing unnecessary sensor data before it’s processed. This helps reduce the need for powerful computers and makes autonomous driving more feasible. |
Keywords
» Artificial intelligence » Feature extraction » Multi modal » Pruning
