Summary of Learning the Bitter Lesson: Empirical Evidence From 20 Years Of Cvpr Proceedings, by Mojtaba Yousefi et al.
Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings
by Mojtaba Yousefi, Jack Collins
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 The study examines how Computer Vision and Pattern Recognition (CVPR) research aligns with the “bitter lesson” principles proposed by Rich Sutton. Using large language models, researchers analyzed two decades of CVPR abstracts and titles to assess the field’s adoption of these principles. The methodology employs state-of-the-art natural language processing techniques to evaluate the evolution of research approaches in computer vision. The results show significant trends in the use of general-purpose learning algorithms and increased computational resources. This work discusses the implications for future computer vision research and its potential impact on broader artificial intelligence development, offering insights that may guide future research priorities and methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computer vision research has changed over time. Researchers analyzed papers from a big conference called CVPR to see if they followed some important principles. They used special computers to help them do this. What they found was that researchers have been using more powerful computers and new ways of learning to make their work better. This might help us make even better computers in the future. |
Keywords
* Artificial intelligence * Natural language processing * Pattern recognition
