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

• URL: http://arxiv.org/abs/2410.09649v1
• Date: Sat, 12 Oct 2024 21:06:13 GMT
• Title: Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings
• Authors: Mojtaba Yousefi, Jack Collins,
• Abstract summary: This study examines the alignment of emphConference 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.
• Score: 1.3812010983144802
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques [52.71395121577439]
  This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR) It highlights the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.
  arXiv  Detail & Related papers  (2024-11-03T18:01:50Z)
• A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications [20.54846023209402]
  This survey focuses on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. We delve into self-supervised learning methods that enable the robust representation of brain signals. We also explore emerging discriminative methods, including graph neural networks (GNN), foundation models, and large language models (LLMs)-based approaches. The survey provides an extensive overview of these cutting-edge techniques, their current applications, and the profound implications they hold for future research and clinical practice.
  arXiv  Detail & Related papers  (2024-09-26T08:09:15Z)
• Frontiers of Deep Learning: From Novel Application to Real-World Deployment [3.3813152538225135]
  This report studies two research papers that represent recent progress on deep learning. The first paper applied the transformer networks, which are typically used in language models, to improve the quality of synthetic aperture radar image. The second paper presents an in-storage computing design solution to enable cost-efficient and high-performance implementations of deep learning recommendation systems.
  arXiv  Detail & Related papers  (2024-07-19T15:11:55Z)
• Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms [5.734290974917728]
  This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms.
  arXiv  Detail & Related papers  (2023-12-20T09:37:06Z)
• Overview of Class Activation Maps for Visualization Explainability [0.0]
  Class Activation Maps (CAMs) enhance interpretability and insights into the decision-making process of deep learning models. This work presents a comprehensive overview of the evolution of Class Activation Maps over time. It also explores the metrics used for evaluating CAMs and introduces auxiliary techniques to improve the saliency of these methods.
  arXiv  Detail & Related papers  (2023-09-25T17:20:51Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• Machine Psychology [54.287802134327485]
  We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology. We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
  arXiv  Detail & Related papers  (2023-03-24T13:24:41Z)
• Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
  Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
  arXiv  Detail & Related papers  (2022-11-27T13:07:14Z)
• Interactive Machine Learning: A State of the Art Review [0.0]
  We provide a comprehensive analysis of the state-of-the-art of interactive machine learning (iML) Research works on adversarial black-box attacks and corresponding iML based defense system, exploratory machine learning, resource constrained learning, and iML performance evaluation are analyzed.
  arXiv  Detail & Related papers  (2022-07-13T13:43:16Z)
• Deep Learning to See: Towards New Foundations of Computer Vision [88.69805848302266]
  This book criticizes the supposed scientific progress in the field of computer vision. It proposes the investigation of vision within the framework of information-based laws of nature.
  arXiv  Detail & Related papers  (2022-06-30T15:20:36Z)
• Threat of Adversarial Attacks on Deep Learning in Computer Vision: Survey II [86.51135909513047]
  Deep Learning is vulnerable to adversarial attacks that can manipulate its predictions. This article reviews the contributions made by the computer vision community in adversarial attacks on deep learning. It provides definitions of technical terminologies for non-experts in this domain.
  arXiv  Detail & Related papers  (2021-08-01T08:54:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.