Summary of Neural Field Classifiers Via Target Encoding and Classification Loss, by Xindi Yang et al.
Neural Field Classifiers via Target Encoding and Classification Loss
by Xindi Yang, Zeke Xie, Xiong Zhou, Boyu Liu, Buhua Liu, Yi Liu, Haoran Wang, Yunfeng Cai, Mingming Sun
First submitted to arxiv on: 2 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 In this research paper, the authors investigate whether regression models are indeed better suited for neural field methods in computer vision and graphics tasks like novel view synthesis and geometry reconstruction. They propose a novel Neural Field Classifier (NFC) framework that transforms existing Neural Field Regressor (NFR) models into classification tasks using a Target Encoding module. This approach optimizes a classification loss, demonstrating impressive effectiveness with minimal extra computational costs. The authors also show the robustness of NFC to sparse inputs, corrupted images, and dynamic scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machine learning methods can be used to improve computer graphics and vision tasks like creating new views of objects or reconstructing 3D shapes from pictures. The researchers ask if using “regression” models (which predict continuous values) is better than using “classification” models (which predict categories). They come up with a new idea called Neural Field Classifier that can change existing regression models into classification models, and show that this works well without requiring much extra computing power. |
Keywords
* Artificial intelligence * Classification * Machine learning * Regression
