计算机视觉中的公平性与偏见缓解：综述
Fairness and Bias Mitigation in Computer Vision: A Survey
AI总结：
本文综述了计算机视觉中公平性与偏见缓解的研究，涵盖公平性定义、偏见发现与分析、缓解方法、相关资源与数据集，并讨论了多模态基础模型和生成模型背景下的趋势与不足。
摘要：
过去二十年间，计算机视觉系统因该领域的多项进展而取得了快速发展。随着这些系统越来越多地被部署在高风险的真实世界应用中，迫切需要确保它们不会传播或放大历史数据或人工整理数据中的任何歧视性倾向，也不会无意中从虚假相关性中学习偏见。本文提出了一项关于公平性的全面综述，总结并阐明了计算机视觉背景下当前的趋势与成果。我们讨论的主题包括：1）源自更广泛的公平机器学习文献及相邻学科的公平性的起源与技术定义。2）旨在发现和分析计算机视觉系统中偏见的工作。3）对近年来提出的用于缓解计算机视觉系统偏见的方法的总结。4）对研究人员为测量、分析和缓解偏见并增强公平性而制作的资源和数据集的全面总结。5）对该领域成功、在多模态基础模型和生成模型背景下的持续趋势以及仍需解决的差距的讨论。所呈现的特征描述应有助于研究人员理解识别和缓解计算机视觉中偏见的重要性以及该领域的现状，并确定未来研究的潜在方向。
英文摘要：
Computer vision systems have witnessed rapid progress over the past two decades due to multiple advances in the field. As these systems are increasingly being deployed in high-stakes real-world applications, there is a dire need to ensure that they do not propagate or amplify any discriminatory tendencies in historical or human-curated data or inadvertently learn biases from spurious correlations. This paper presents a comprehensive survey on fairness that summarizes and sheds light on ongoing trends and successes in the context of computer vision. The topics we discuss include 1) The origin and technical definitions of fairness drawn from the wider fair machine learning literature and adjacent disciplines. 2) Work that sought to discover and analyze biases in computer vision systems. 3) A summary of methods proposed to mitigate bias in computer vision systems in recent years. 4) A comprehensive summary of resources and datasets produced by researchers to measure, analyze, and mitigate bias and enhance fairness. 5) Discussion of the field's success, continuing trends in the context of multimodal foundation and generative models, and gaps that still need to be addressed. The presented characterization should help researchers understand the importance of identifying and mitigating bias in computer vision and the state of the field and identify potential directions for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
