RI: Small: Hierarchical Feature Learning by Heterogeneous Networks with Application to Face Verification

学习好的特征是识别人脸和理解场景等计算机视觉问题的关键。许多计算机视觉研究人员通过为大型数据库中的每个图像提供语义标签来学习特征，将每个图像的信息量限制在几个比特。其他人通过识别图像中的常见模式（如线条、斑点和更复杂的形状）来学习特征，但忽略语义信息。该项目开发算法来学习图像中常见的特征，并使用一种称为异构网络的新型深度神经网络来预测不同空间尺度下图像的语义。所开发的算法允许在中间层合并语义信息。所开发的算法不仅可以改变特征的学习方式，还可以指示如何将特征学习扩展到数百万图像的大型数据集。研究小组使用NCSA的千万亿次超级计算机Blue Waters和两个大规模（数百万张图像）图像数据集来解决人脸验证中的挑战性问题。本课题的研究与本科和研究生教育相结合。本课题的研究成果在计算机视觉和模式识别等领域具有广泛的应用前景。研究小组计划在完成后将该项目收集的算法和面部数据集发布给研究社区。

Learning good features is a key to computer vision problems such as recognizing human faces, and understanding scenes. Many computer vision researchers learn features by providing a semantic label for each image in a large database, limiting the amount of information per image to a few bits. Others learn features by identifying common patterns found in images such as lines, blobs, and more complicated shapes, but ignoring semantic information. This project develops algorithms to learn features that are common in images and also predict the semantics of images at various spatial scales using a new type of deep neural network called Heterogeneous Networks. The developed algorithms allow the incorporation of semantic information at intermediate layers. The algorithms developed can not only change how features are learned but also indicate how to scale feature learning to giant datasets of millions of images. The research team addresses challenging problems in human face verification using NCSA's petascale supercomputer, Blue Waters, and two large scale (millions of images) image data sets. The research of this projected is integrated with both undergraduate and graduate education. The results obtained from this project are applicable to a wide range of applications in computer vision and pattern recognition. The research team plans to release algorithms and face data sets collected in this project to research communities once they are finished.