Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings (2024)

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.

## conclusion our study examined the alignment of cvpr research with rich sutton's "the bitter lesson" over twenty years, leveraging large language models to analyze trends. the findings reveal a consistent increase in the adoption of general-purpose learning algorithms and scalability with computational resources, reflecting a strong adherence to the core principles of the "bitter lesson." these trends underscore the machine learning community's preference for data-driven and computationheavy approaches over manual engineering and domain-specific knowledge. however, the dimension of "search over heuristics" has not experienced a similar upward trajectory, indicating limited integration of search-based methodologies within the field. this stagnation contrasts with recent advancements in inference-time scaling, exemplified by openai's o1 models, which emphasize the importance of test-time compute in overcoming diminishing returns. the o1 models' ability to simulate various strategies and scenarios during inference, similar to alphago's monte carlo tree search (mcts), marks a key departure from earlier approaches that relied heavily on large pre-trained models. the paradigm shift towards scaling inference time, driven by the development of larger and more complex models, has the potential to emulate search-like processes. as computational capabilities continue to expand, it is plausible that future research may increasingly incorporate search techniques, thereby enhancing alignment with this dimension of the "bitter lesson." the dynamic resource allocation in o1 models, which adjusts computational resources based on task complexity, further underscores the potential for integrating search methodologies. overall, our findings highlight the continued relevance of the "bitter lesson" in shaping the trajectory of computer vision research. by emphasizing generality and scalability, the field is wellpositioned to leverage emerging computational advancements. future work should explore the integration of search methodologies and assess their impact on research impact and innovation within computer vision, particularly in light of recent breakthroughs in inference-time scaling.