Summary of Respike: Residual Frames-based Hybrid Spiking Neural Networks For Efficient Action Recognition, by Shiting Xiao et al.
ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
by Shiting Xiao, Yuhang Li, Youngeun Kim, Donghyun Lee, Priyadarshini Panda
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed ReSpike framework leverages the strengths of Artificial Neural Networks (ANNs) and Spiking Neural Networks (SNNs) to achieve high accuracy and low energy cost in action recognition tasks. The hybrid architecture decomposes film clips into spatial and temporal components, using ANNs for learning spatial information and SNNs for learning temporal information. A multi-scale cross-attention mechanism enables effective feature fusion. Compared to state-of-the-art SNN baselines, ReSpike demonstrates significant performance improvements on HMDB-51, UCF-101, and Kinetics-400. The framework achieves comparable performance with prior ANN approaches while providing a better accuracy-energy tradeoff. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReSpike is a new way to recognize actions in videos using both old and new types of artificial neural networks. This approach helps computers understand what’s happening in movies by breaking them down into smaller parts, like pictures and short video clips. It uses special networks that are good at learning about things that happen over time, and others that are good at understanding what’s happening in each picture. By combining these strengths, ReSpike does a better job than other approaches at recognizing actions while also using less energy. |
Keywords
» Artificial intelligence » Cross attention
