Summary of Retain, Blend, and Exchange: a Quality-aware Spatial-stereo Fusion Approach For Event Stream Recognition, by Lan Chen et al.
Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition
by Lan Chen, Dong Li, Xiao Wang, Pengpeng Shao, Wei Zhang, Yaowei Wang, Yonghong Tian, Jin Tang
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel dual-stream framework, EFV++, is proposed for event stream-based pattern recognition. The model learns features from two common event representations: event images and event voxels. Transformer and Graph Neural Network (GNN) are used to learn spatial and three-dimensional stereo information separately. A differentiated fusion approach is employed to retain high-quality features, blend medium-quality features, and exchange low-quality features. The enhanced dual features are fed into a fusion Transformer along with bottleneck features. A novel hybrid interaction readout mechanism is introduced to enhance the diversity of final representations. Experimental results demonstrate state-of-the-art performance on multiple event stream-based classification datasets, including Bullying10k, achieving 90.51%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of recognizing patterns in event streams is developed. This method uses two types of information: what’s happening now and what’s happened before. It’s like looking at a video and understanding the action by combining what you see with what you’ve seen before. The approach combines the strengths of different methods to make better predictions. It works well on several datasets, including one that tracks bullying behavior, where it gets the best results yet. |
Keywords
» Artificial intelligence » Classification » Gnn » Graph neural network » Pattern recognition » Transformer
