Summary of Domain-guided Masked Autoencoders For Unique Player Identification, by Bavesh Balaji et al.
Domain-Guided Masked Autoencoders for Unique Player Identification
by Bavesh Balaji, Jerrin Bright, Sirisha Rambhatla, Yuhao Chen, Alexander Wong, John Zelek, David A Clausi
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel domain-guided masked autoencoder (d-MAE) is a superior alternative to conventional feature extractors for robustly extracting features from broadcast videos with motion blur, low resolution, and occlusions. This model enhances unique player identification in vision-driven sports analytics by leveraging a spatio-temporal network that incorporates the d-MAE. The authors conduct experiments on three large-scale sports datasets (baseball, SoccerNet, and ice hockey) and demonstrate significant improvements over state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research paper, scientists developed a new way to identify specific players in sports videos. They created a special kind of machine learning model called the domain-guided masked autoencoder (d-MAE). This model is better at recognizing features in videos that are blurry or have low resolution. The researchers used this model to develop a system for identifying unique players, and they tested it on three large datasets from different sports. |
Keywords
» Artificial intelligence » Autoencoder » Machine learning » Mae
