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.
翻译:大型数据集对于图像检索方面的深层学习取得成功至关重要,然而,人工评估错误和半监督的批注技术可能导致即使在流行数据集中也出现标签噪音。由于以前的工作主要是研究图像分类任务的说明质量,因此仍然不清楚标签噪音如何影响对图像检索的深层学习方法。在这项工作中,我们显示图像检索方法对标签噪音的力度比图像分类方法要小。此外,我们第一次调查与图像检索任务相关的不同类型的标签噪音,并研究其对模型性能的影响。