论文标题
深入研究高质量的合成面遮挡细分数据集
Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
论文作者
论文摘要
本文在数据集上进行了全面的分析，以进行遮挡感知面部细分，这对于许多下游应用程序至关重要。此类数据集的收集和注释是耗时且劳动密集型的。尽管在合成数据生成中已经做出了一些努力，但数据的自然主义方面仍然不那么探索。在我们的研究中，我们提出了两种遮挡产生技术，即自然主义的闭塞产生（NATOCC），用于产生高质量的自然主义合成闭塞面；和随机闭塞生成（RANDOCC），这是一种更通用的合成封闭数据生成方法。我们从经验上表明了这两种方法的有效性和鲁棒性，即使对于看不见的遮挡也是如此。为了促进模型评估，我们提出了两个高分辨率的现实世界遮挡的面部数据集，具有细粒度的注释，RealoCC和Realocc妻子，既有仔细的预处理预处理，又有一个用于鲁棒性测试的野外设置。我们进一步对新引入的细分基准进行了全面分析，为将来的探索提供了见解。
This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications. The collection and annotation of such datasets are time-consuming and labor-intensive. Although some efforts have been made in synthetic data generation, the naturalistic aspect of data remains less explored. In our study, we propose two occlusion generation techniques, Naturalistic Occlusion Generation (NatOcc), for producing high-quality naturalistic synthetic occluded faces; and Random Occlusion Generation (RandOcc), a more general synthetic occluded data generation method. We empirically show the effectiveness and robustness of both methods, even for unseen occlusions. To facilitate model evaluation, we present two high-resolution real-world occluded face datasets with fine-grained annotations, RealOcc and RealOcc-Wild, featuring both careful alignment preprocessing and an in-the-wild setting for robustness test. We further conduct a comprehensive analysis on a newly introduced segmentation benchmark, offering insights for future exploration.
