Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings

TL;DR

This study examines the alignment of Conference on Computer Vision and Pattern Recognition research with the principles of the “bitter lesson” proposed by Rich Sutton, and leverages state-of-the-art natural language processing techniques to systematically evaluate the evolution of research approaches in computer vision.

Abstract

This study examines the alignment of \emph{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.

Figures (4)

• Figure 1: Total number of CVPR papers present in database for each year from 2005 to 2024.
• Figure 2: Distribution of citation counts and log-transformed citation counts for CVPR papers from 2005 to 2024 present in the database.
• Figure 3: Comparison of ICC and Krippendorff's alpha values across the five dimensions of "bitter lesson" alignment for the three language models used in the study.
• Figure 4: Line plot showing the average alignment scores across years for CVPR papers from 2005 to 2024.