Summary of A Short Note on Evaluating Repnet For Temporal Repetition Counting in Videos, by Debidatta Dwibedi et al.
A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos
by Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 This paper investigates the inconsistencies in evaluating RepNet, a machine learning model, across various studies. To address these issues, the authors provide performance results on different datasets and release evaluation code and a checkpoint to obtain these results. This study aims to improve the transparency and reproducibility of RepNet evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RepNet is a machine learning model that has been used in various studies, but there are some inconsistencies in how it has been evaluated. The authors of this paper want to fix this by providing more information about how well RepNet works on different datasets and releasing the code and data needed to get these results. |
Keywords
* Artificial intelligence * Machine learning
