Summary of Cpfd: Confidence-aware Privileged Feature Distillation For Short Video Classification, by Jinghao Shi et al.
CPFD: Confidence-aware Privileged Feature Distillation for Short Video Classification
by Jinghao Shi, Xiang Shen, Kaili Zhao, Xuedong Wang, Vera Wen, Zixuan Wang, Yifan Wu, Zhixin Zhang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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 proposed Confidence-aware Privileged Feature Distillation (CPFD) approach aims to integrate dense features customized for specific business scenarios with end-to-end multi-modal models. By leveraging confidence scores from the teacher model, CPFD adaptively mitigates performance variance between the student and teacher models. The authors demonstrate improved video classification F1 scores by 6.76% compared to end-to-end multimodal-models and reduce the performance gap by 84.6%. The framework has been successfully deployed in production systems for over a dozen models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to combine two types of features: dense features customized for specific business scenarios, and end-to-end multi-modal features. This combination aims to make video classification more efficient while keeping the valuable information from historical data. The authors developed an algorithm called Confidence-aware Privileged Feature Distillation (CPFD) that adapts to different situations during training. They tested CPFD on five tasks and found it outperformed other methods, making it suitable for real-world applications. |
Keywords
» Artificial intelligence » Classification » Distillation » Multi modal » Teacher model
