Summary of Understanding Matrix Function Normalizations in Covariance Pooling Through the Lens Of Riemannian Geometry, by Ziheng Chen et al.
Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry
by Ziheng Chen, Yue Song, Xiao-Jun Wu, Gaowen Liu, Nicu Sebe
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper explores the use of Global Covariance Pooling (GCP) in Deep Neural Networks (DNNs), demonstrating its ability to improve performance by leveraging second-order statistics. The authors highlight that existing methods, such as matrix logarithm and power normalization followed by Euclidean classification, are not well-understood from a Riemannian geometry perspective. To address this gap, the paper provides a unified understanding of matrix functions in GCP, interpreting their mechanism through tangent classifiers (Euclidean) and Riemannian classifiers. The authors validate their findings through extensive experiments on fine-grained and large-scale visual classification datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GCP is a technique that helps deep learning models work better by using information about how different parts of an image are related to each other. Right now, people don’t fully understand why this works so well, but this paper tries to fill in the gaps. The authors show that the way we normally use GCP isn’t actually the best way, and they propose a new way of understanding it based on something called Riemannian geometry. They test their ideas on lots of images and find that they work really well. |
Keywords
» Artificial intelligence » Classification » Deep learning
