论文标题
对美的追求：将图像标签转换为有意义的向量
The pursuit of beauty: Converting image labels to meaningful vectors
论文作者
论文摘要
计算机视觉社区的一个挑战是了解图像的语义，以允许基于现有的高级功能或更好地分析（半）标记的数据集的图像重建。为了应对这一挑战，本文介绍了一种称为基于咬合的潜在表示（OLR）的方法，用于将图像标签转换为有意义的表示，以捕获大量数据语义。除了信息丰富之外，这些表示形式还构成了一个分离的低维潜在空间，每个图像标签都被编码为单独的向量。我们在一系列实验中评估了这些表示的质量，结果表明该模型可以捕获数据概念并发现数据相互关系。
A challenge of the computer vision community is to understand the semantics of an image, in order to allow image reconstruction based on existing high-level features or to better analyze (semi-)labelled datasets. Towards addressing this challenge, this paper introduces a method, called Occlusion-based Latent Representations (OLR), for converting image labels to meaningful representations that capture a significant amount of data semantics. Besides being informational rich, these representations compose a disentangled low-dimensional latent space where each image label is encoded into a separate vector. We evaluate the quality of these representations in a series of experiments whose results suggest that the proposed model can capture data concepts and discover data interrelations.
