Summary of Generalized Relevance Learning Grassmann Quantization, by M. Mohammadi et al.
Generalized Relevance Learning Grassmann Quantization
by M. Mohammadi, M. Babai, M.H.F. Wilkinson
First submitted to arxiv on: 14 Mar 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 The proposed Generalized Relevance Learning Vector Quantization (GRLVQ) model is an extension of the subspace-based approach for image-set classification. It returns a set of prototype subspaces and relevance vectors, which provide insights into the model’s decisions by highlighting influential images and pixels for predictions. Unlike previous works, the model complexity during inference is independent of dataset size due to learning prototypes. The GRLVQ model outperforms previous works with lower complexity in several recognition tasks, including handwritten digit recognition, face recognition, activity recognition, and object recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to classify groups of images or videos taken under different conditions. It uses something called the Grassmann manifold to help the computer understand what’s important in each image. The model is called Generalized Relevance Learning Vector Quantization (GRLVQ). It’s good at recognizing things like handwritten digits, faces, activities, and objects even when they’re taken in different styles or lighting conditions. |
Keywords
* Artificial intelligence * Activity recognition * Classification * Face recognition * Inference * Quantization
