论文标题
带有层次标签的图像的学习表示形式
Learning Representations For Images With Hierarchical Labels
论文作者
论文摘要
已经对图像分类进行了广泛的研究，但是在使用非规定的外部指导方向上的工作有限，而除了传统的图像标签对训练此类模型之外。在本文中，我们提供了一组方法，以利用类标签引起的语义层次结构的信息。在论文的第一部分中，我们将标签层次结构知识注入任意分类器，并从经验上表明，此类外部语义信息的可用性与图像中的视觉语义结合使用，从而提高了整体性能。朝这个方向迈出一步，我们通过使用基于订单的基于嵌入式的模型（以自然语言为普遍）更明确地对标签标签标签和标签图像相互作用进行建模，并将其定制到计算机视觉范围内以执行图像分类。 Although, contrasting in nature, both the CNN-classifiers injected with hierarchical information, and the embedding-based models outperform a hierarchy-agnostic model on the newly presented, real-world ETH Entomological Collection image dataset https://www.research-collection.ethz.ch/handle/20.500.11850/365379.
Image classification has been studied extensively but there has been limited work in the direction of using non-conventional, external guidance other than traditional image-label pairs to train such models. In this thesis we present a set of methods to leverage information about the semantic hierarchy induced by class labels. In the first part of the thesis, we inject label-hierarchy knowledge to an arbitrary classifier and empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance. Taking a step further in this direction, we model more explicitly the label-label and label-image interactions by using order-preserving embedding-based models, prevalent in natural language, and tailor them to the domain of computer vision to perform image classification. Although, contrasting in nature, both the CNN-classifiers injected with hierarchical information, and the embedding-based models outperform a hierarchy-agnostic model on the newly presented, real-world ETH Entomological Collection image dataset https://www.research-collection.ethz.ch/handle/20.500.11850/365379.
