Abstract
In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world applications, such as image captioning and news video analysis. Associated with this task, we design and provide concrete measurement set, evaluation protocol, as well as training data. We also present in details our experiment setup and report promising baseline results. Our benchmark task could lead to one of the largest classification problems in computer vision. To the best of our knowledge, our training dataset, which contains 10M images in version 1, is the largest publicly available one in the world.
Abstract (translated by Google)
在本文中，我们设计了一个基准任务，并提供相关数据集来识别人脸图像，并将其链接到知识库中相应的实体关键字。更具体地说，我们提出了一个基准任务，从他们的脸部图像中识别出100万名名人，将所有可能收集的这个人在网络上的脸部图像用作训练数据。知识库提供的丰富信息有助于消除歧义，提高识别的准确性，并有助于图像字幕和新闻视频分析等各种现实应用。与此相关的任务，我们设计并提供具体的测量集，评估协议，以及培训数据。我们还详细介绍了我们的实验设置并报告了有希望的基线结果。我们的基准任务可能会导致计算机视觉中最大的分类问题之一。就我们所知，我们的训练数据集（包含版本1中的10M图像）是世界上最大的公开可用数据集。
URL
https://arxiv.org/abs/1607.08221