Summary of Enhanced Temporal Processing in Spiking Neural Networks For Static Object Detection Using 3d Convolutions, by Huaxu He
Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions
by Huaxu He
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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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 Directly trained Spiking Neural Networks (SNNs) have shown promising results in classification tasks, matching or surpassing traditional Artificial Neural Networks (ANNs). However, a significant performance gap remains when SNNs are tested on frame-based static object datasets like COCO2017. To bridge this gap and leverage the energy efficiency advantages of SNNs, this paper proposes enhancing the spatiotemporal processing capabilities of SNNs by replacing traditional 2D convolutions with 3D convolutions. The approach also incorporates temporal information recurrence mechanisms within neurons to improve their utilization of temporal data. Experimental results demonstrate that directly trained SNNs using this method achieve performance levels comparable to ANNs on COCO2017 and VOC datasets, addressing the key challenge in developing SNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving Spiking Neural Networks (SNNs), a type of computer network inspired by how our brains work. Right now, these networks are good at some tasks but not others. They can do well on simple classification problems, like recognizing images. However, when it comes to more complex tasks, like detecting objects in pictures, they still don’t perform as well as other types of networks. The goal of this research is to make SNNs better at processing information over time, which will allow them to be used for a wider range of applications. To do this, the researchers propose some new techniques that combine information from different time points and incorporate more temporal data into the network. The results show that these new approaches can help SNNs perform as well as other networks on certain tasks. |
Keywords
» Artificial intelligence » Classification » Spatiotemporal
