Large-scale datasets have played a crucial role in the advancement of computer vision. However, they often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs, which can inhibit model performance and reduce trustworthiness. With the advocacy of data-centric research, various data-centric solutions have been proposed to solve the dataset problems mentioned above. They improve the quality of datasets by re-organizing them, which we call dataset refinement. In this survey, we provide a comprehensive and structured overview of recent advances in dataset refinement for problematic computer vision datasets. Firstly, we summarize and analyze the various problems encountered in large-scale computer vision datasets. Then, we classify the dataset refinement algorithms into three categories based on the refinement process: data sampling, data subset selection, and active learning. In addition, we organize these dataset refinement methods according to the addressed data problems and provide a systematic comparative description. We point out that these three types of dataset refinement have distinct advantages and disadvantages for dataset problems, which informs the choice of the data-centric method appropriate to a particular research objective. Finally, we summarize the current literature and propose potential future research topics.

翻译：大规模数据集在计算机视觉的发展中发挥了关键作用。然而，这些数据集常存在类别不平衡、噪声标签、数据集偏差或高资源成本等问题，这会抑制模型性能并降低其可信度。随着以数据为中心的研究的倡导，人们提出了多种以数据为中心的解决方案来解决上述数据集问题。这些方案通过重新组织数据集来提升数据质量，我们称之为数据集精炼。在本综述中，我们针对存在问题的计算机视觉数据集，全面且结构化地概述了数据集精炼领域的最新进展。首先，我们总结并分析了大规模计算机视觉数据集中遇到的各种问题。然后，根据精炼过程将数据集精炼算法分为三类：数据采样、数据子集选择和主动学习。此外，我们按照所解决的数据问题对这些数据集精炼方法进行了整理，并提供了系统的比较描述。我们指出，这三类数据集精炼在处理数据集问题时各有优劣，这为根据特定研究目标选择合适的数据中心方法提供了参考。最后，我们总结了当前文献并提出了未来潜在的研究方向。