Summary of Evrepsl: Event-stream Representation Via Self-supervised Learning For Event-based Vision, by Qiang Qu et al.
EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision
by Qiang Qu, Xiaoming Chen, Yuk Ying Chung, Yiran Shen
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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 paper introduces a data-driven approach to enhance the quality of event-stream representations in computer vision. The goal is to convert asynchronous event streams into a formatted structure for conventional machine learning models. The authors propose EvRep, a new event-stream representation based on spatial-temporal statistics, and train a self-supervised representation generator, RepGen, using EvRep as input. This leads to high-quality representations, termed EvRepSL, which outperform existing methods in various event-based classification and optical flow datasets captured with different event cameras. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to improve the quality of event-stream representations in computer vision. It helps computers understand images from special cameras that capture events instead of frames. The approach uses math and learning algorithms to make the camera data better for machines to use. This can help with tasks like recognizing objects or tracking motion. The results show this method is better than others and works well with different cameras and tasks. |
Keywords
» Artificial intelligence » Classification » Machine learning » Optical flow » Self supervised » Tracking
