Summary of Hummuss: Human Motion Understanding Using State Space Models, by Arnab Kumar Mondal et al.
HumMUSS: Human Motion Understanding using State Space Models
by Arnab Kumar Mondal, Stefano Alletto, Denis Tome
First submitted to arxiv on: 16 Apr 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 A novel attention-free spatiotemporal model for human motion understanding is proposed, which not only matches the performance of transformer-based models in various tasks but also brings added benefits such as adaptability to different frame rates and enhanced training speed. The model is designed to overcome the limitations of transformer-based approaches, which are slower when predicting on a continuous stream of frames in real-time and do not generalize well to new frame rates. By leveraging recent advancements in state space models, the proposed model supports both offline and real-time applications, with improved memory efficiency and speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to understand human movement from videos. They wanted to improve on existing methods that use transformers, which are slow when processing many frames at once and don’t work well with different frame rates. Their new model is fast, efficient, and can handle varying frame rates. It’s also good at predicting movements in real-time, making it useful for applications like video analysis and action recognition. |
Keywords
» Artificial intelligence » Attention » Spatiotemporal » Transformer
