Images can be censored by masking the region(s) of interest with a solid color or pattern. When a censored image is used for classification or matching, the mask itself may impact the results. Recent work in image inpainting and data augmentation provide two different approaches for dealing with censored images. In this paper, we perform an extensive evaluation of these methods to understand if the impact of censoring can be mitigated for image classification and retrieval. Results indicate that modern learning-based inpainting approaches outperform augmentation strategies and that metrics typically used to evaluate inpainting performance (e.g., reconstruction accuracy) do not necessarily correspond to improved classification or retrieval, especially in the case of person-shaped masked regions.

中文翻译：

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审查图像查询的修复和增强评估

可以通过用纯色或图案掩盖感兴趣的区域来审查图像。当审查图像用于分类或匹配时，掩码本身可能会影响结果。最近在图像修复和数据增强方面的工作提供了两种不同的处理删失图像的方法。在本文中，我们对这些方法进行了广泛的评估，以了解是否可以减轻审查对图像分类和检索的影响。结果表明，现代基于学习的修复方法优于增强策略，并且通常用于评估修复性能的指标（例如，重建精度）不一定对应于改进的分类或检索，尤其是在人形掩蔽区域的情况下。