Summary of Cue-net: Violence Detection Video Analytics with Spatial Cropping, Enhanced Uniformerv2 and Modified Efficient Additive Attention, by Damith Chamalke Senadeera et al.
CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention
by Damith Chamalke Senadeera, Xiaoyun Yang, Dimitrios Kollias, Gregory Slabaugh
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper introduces CUE-Net, a novel architecture designed for automated violence detection in video surveillance. By combining spatial Cropping with an enhanced UniformerV2 architecture, convolutional and self-attention mechanisms, and a Modified Efficient Additive Attention mechanism, CUE-Net effectively identifies violent activities while reducing quadratic time complexity. This approach addresses traditional challenges like capturing distant or partially obscured subjects within video frames. CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to find violence in videos using cameras. It’s hard to look at lots of video data quickly, so this method tries to fix that. It combines different ideas from computer vision and uses attention mechanisms to focus on important parts of the video. This helps it work better when things are far away or partly hidden. The new approach does really well on two big datasets, beating what’s already out there. |
Keywords
» Artificial intelligence » Attention » Self attention
