Title: A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild Show Chinese title

Title: 一个用于检测野外儿童的手动标注图像-标题数据集

Abstract: Platforms and the law regulate digital content depicting minors (defined as individuals under 18 years of age) differently from other types of content. Given the sheer amount of content that needs to be assessed, machine learning-based automation tools are commonly used to detect content depicting minors. To our knowledge, no dataset or benchmark currently exists for detecting these identification methods in a multi-modal environment. To fill this gap, we release the Image-Caption Children in the Wild Dataset (ICCWD), an image-caption dataset aimed at benchmarking tools that detect depictions of minors. Our dataset is richer than previous child image datasets, containing images of children in a variety of contexts, including fictional depictions and partially visible bodies. ICCWD contains 10,000 image-caption pairs manually labeled to indicate the presence or absence of a child in the image. To demonstrate the possible utility of our dataset, we use it to benchmark three different detectors, including a commercial age estimation system applied to images. Our results suggest that child detection is a challenging task, with the best method achieving a 75.3% true positive rate. We hope the release of our dataset will aid in the design of better minor detection methods in a wide range of scenarios. Show Chinese abstract

Abstract: 平台和法律对涉及未成年人（定义为18岁以下的个人）的数字内容与其他类型的内容进行不同的监管。 鉴于需要评估的内容量巨大，通常使用基于机器学习的自动化工具来检测涉及未成年人的内容。 据我们所知，目前没有数据集或基准可以用于多模态环境中检测这些识别方法。 为填补这一空白，我们发布了Image-Caption Children in the Wild Dataset (ICCWD)，这是一个图像-标题数据集，旨在为检测未成年人描绘的工具提供基准测试。 我们的数据集比以前的儿童图像数据集更丰富，包含各种背景下的儿童图像，包括虚构的描绘和部分可见的身体。 ICCWD包含10,000个手动标记的图像-标题对，用于指示图像中是否存在儿童。 为了展示我们数据集的潜在用途，我们用它来评估三种不同的检测器，其中包括应用于图像的商业年龄估计系统。 我们的结果显示，儿童检测是一项具有挑战性的任务，最佳方法的真正阳性率为75.3%。 我们希望发布我们的数据集将有助于设计更好的儿童检测方法，适用于各种场景。