Summary of Crest: An Efficient Conjointly-trained Spike-driven Framework For Event-based Object Detection Exploiting Spatiotemporal Dynamics, by Ruixin Mao et al.
CREST: An Efficient Conjointly-trained Spike-driven Framework for Event-based Object Detection Exploiting Spatiotemporal Dynamics
by Ruixin Mao, Aoyu Shen, Lin Tang, Jun Zhou
First submitted to arxiv on: 17 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 This paper proposes CREST, a novel framework for event-based object detection using Spiking Neural Networks (SNNs). The authors address the limitations of existing SNN frameworks by introducing a conjointly-trained spike-driven approach that exploits spatiotemporal dynamics. This method accelerates SNN learning, alleviates gradient vanishing, and supports dual operation modes for flexible hardware implementation. CREST also features a multi-scale spatiotemporal event integrator (MESTOR) and a spatiotemporal-IoU (ST-IoU) loss function. The authors demonstrate the effectiveness of their approach on three datasets, achieving superior object recognition and detection performance with up to 100X energy efficiency compared to state-of-the-art SNN algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to use computers that are good at detecting objects using cameras that can take many pictures very quickly. The problem is that these computer models need to be trained, but it’s hard because they get stuck in loops and use too much energy. The authors came up with a solution called CREST that helps the computer learn faster and more efficiently. It also helps the computer look at objects from different angles and sizes, which makes it better at detecting things. They tested their new approach on some pictures and found that it was really good at finding objects, and it used much less energy than other methods. |
Keywords
» Artificial intelligence » Loss function » Object detection » Spatiotemporal
