Summary of Epam-net: An Efficient Pose-driven Attention-guided Multimodal Network For Video Action Recognition, by Ahmed Abdelkawy et al.
EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action Recognition
by Ahmed Abdelkawy, Asem Ali, Aly Farag
First submitted to arxiv on: 10 Aug 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 presents a novel, efficient pose-driven attention-guided multimodal network (EPAM-Net) for action recognition in videos. The proposed model, EPAM-Net, combines RGB and pose streams to capture spatiotemporal features from videos and skeleton sequences. The X-ShiftNet architecture is used to reduce the computational cost of 3D CNNs, enabling efficient learning. Skeleton features guide the visual network stream, focusing on keyframes and salient regions using a spatial-temporal attention block. The predictions from both streams are fused for final classification. Experimental results show that EPAM-Net outperforms state-of-the-art methods on various datasets, with significant reductions in floating-point operations (FLOPs) and network parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to recognize human actions in videos using a special kind of AI model called EPAM-Net. This model uses both the video itself and information about the person’s skeleton to understand what’s happening in the video. The model is really good at recognizing actions, and it does this while using much less computer power than other models that do similar things. This makes it useful for real-time applications where you need to quickly analyze videos. |
Keywords
» Artificial intelligence » Attention » Classification » Spatiotemporal
