Summary of Video Annotator: a Framework For Efficiently Building Video Classifiers Using Vision-language Models and Active Learning, by Amir Ziai et al.
Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learning
by Amir Ziai, Aneesh Vartakavi
First submitted to arxiv on: 9 Feb 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 This research paper proposes innovative approaches to improve data annotation for machine learning models. By leveraging domain experts’ expertise, the authors aim to reduce reliance on third-party annotators and increase the accuracy and consistency of annotations. The study focuses on addressing the challenges of labeling hard samples, which are crucial for model training but notoriously difficult to annotate. The authors develop new methods to handle these challenging instances efficiently, minimizing the need for feedback rounds and iterations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier and more accurate to label data for machine learning models. Right now, it takes a lot of time and effort to do this, and sometimes we have to rely on people who aren’t experts in the field. The researchers are working on ways to make the process better by getting the right people involved from the start. They’re also trying to figure out how to deal with tricky data points that require extra attention. This could lead to more reliable models and less wasted time and money. |
Keywords
* Artificial intelligence * Attention * Machine learning
