Summary of Chimera: a Block-based Neural Architecture Search Framework For Event-based Object Detection, by Diego A. Silva et al.
Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection
by Diego A. Silva, Ahmed Elsheikh, Kamilya Smagulova, Mohammed E. Fouda, Ahmed M. Eltawil
First submitted to arxiv on: 27 Dec 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 Chimera framework is a novel approach for Event-Based Object Detection that leverages Block-Based Neural Architecture Search (NAS) techniques to adapt RGB-domain processing methods to the event domain. This framework combines various macroblocks, such as Attention blocks and State Space Models, to provide a trade-off between local and global processing capabilities. The results on the PErson Detection in Robotics (PEDRo) dataset show comparable performance levels to state-of-the-art models while reducing parameters by an average of 1.6 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Event-based cameras are special sensors that work like human eyes, making them useful for fast and efficient image processing. Deep Learning techniques can help with this task. The Chimera framework is a way to adapt these techniques to event-based data. It does this by combining different blocks of neural networks, which helps balance between local and global processing. The results show that Chimera performs similarly to the best models while using fewer parameters. |
Keywords
» Artificial intelligence » Attention » Deep learning » Object detection
