论文标题
深图像检索对标签噪声不强大
Deep Image Retrieval is not Robust to Label Noise
论文作者
论文摘要
大型数据集对于图像检索中的深度学习成功至关重要。但是，即使在流行的数据集中，手动评估错误和半监督注释技术也可能导致标签噪声。由于以前的工作主要研究了图像分类任务中的注释质量，因此仍不清楚标签噪声如何影响深度学习方法的图像检索方法。在这项工作中，我们表明图像检索方法对标记噪声的鲁棒性不如图像分类。此外，我们首次研究特定于图像检索任务的不同类型的标签噪声并研究其对模型性能的影响。
Large-scale datasets are essential for the success of deep learning in image retrieval. However, manual assessment errors and semi-supervised annotation techniques can lead to label noise even in popular datasets. As previous works primarily studied annotation quality in image classification tasks, it is still unclear how label noise affects deep learning approaches to image retrieval. In this work, we show that image retrieval methods are less robust to label noise than image classification ones. Furthermore, we, for the first time, investigate different types of label noise specific to image retrieval tasks and study their effect on model performance.
