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        <title>Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings</title>
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    <abstract>This study examines the alignment of Conference on Computer Vision and Pattern Recognition (CVPR) research with the principles of the “bitter lesson” proposed by Rich Sutton. We analyze two decades of CVPR abstracts and titles using large language models (LLMs) to assess the field’s embracement of these principles. Our methodology leverages state-of-the-art natural language processing techniques to systematically evaluate the evolution of research approaches in computer vision. The results reveal significant trends in the adoption of general-purpose learning algorithms and the utilization of increased computational resources. We discuss the implications of these findings for the future direction of computer vision research and its potential impact on broader artificial intelligence development. This work contributes to the ongoing dialogue about the most effective strategies for advancing machine learning and computer vision, offering insights that may guide future research priorities and methodologies in the field.</abstract>
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%0 Conference Proceedings
%T Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings
%A Yousefi, Mojtaba
%A Collins, Jack
%Y Peled-Cohen, Lotem
%Y Calderon, Nitay
%Y Lissak, Shir
%Y Reichart, Roi
%S Proceedings of the 1st Workshop on NLP for Science (NLP4Science)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F yousefi-collins-2024-learning
%X This study examines the alignment of Conference on Computer Vision and Pattern Recognition (CVPR) research with the principles of the “bitter lesson” proposed by Rich Sutton. We analyze two decades of CVPR abstracts and titles using large language models (LLMs) to assess the field’s embracement of these principles. Our methodology leverages state-of-the-art natural language processing techniques to systematically evaluate the evolution of research approaches in computer vision. The results reveal significant trends in the adoption of general-purpose learning algorithms and the utilization of increased computational resources. We discuss the implications of these findings for the future direction of computer vision research and its potential impact on broader artificial intelligence development. This work contributes to the ongoing dialogue about the most effective strategies for advancing machine learning and computer vision, offering insights that may guide future research priorities and methodologies in the field.
%U https://aclanthology.org/2024.nlp4science-1.15
%P 175-187

Markdown (Informal)

[Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings](https://aclanthology.org/2024.nlp4science-1.15) (Yousefi & Collins, NLP4Science 2024)

• Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings (Yousefi & Collins, NLP4Science 2024)

ACL

• Mojtaba Yousefi and Jack Collins. 2024. Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings. In Proceedings of the 1st Workshop on NLP for Science (NLP4Science), pages 175–187, Miami, FL, USA. Association for Computational Linguistics.