Semi-Supervised Zero-Shot Classification with Label Representation Learning
Semi-Supervised Zero-Shot Classification with Label Representation Learning
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DOI：
10.1109/iccv.2015.479
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
X. Li;Yuhong Guo;Dale Schuurmans
中科院分区：
文献类型：
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作者：
X. Li;Yuhong Guo;Dale Schuurmans
考虑到收集标记训练数据的挑战，零样本分类（将观察到的类的信息转移到识别未见过的类）在计算机视觉社区中变得越来越流行。大多数现有的零样本学习方法要求用户首先为每个类别提供一组语义视觉属性作为辅助信息，然后再应用引入中间属性预测问题的两步预测过程。在本文中，我们提出了一种新颖的零样本分类方法，该方法可以在半监督大裕度学习框架中自动从输入数据中学习标签嵌入。所提出的框架联合考虑所有类别（观察到的和未看到的）的多类分类，并直接解决目标预测问题，而不引入中间预测问题。它还能够在可用时合并来自不同来源的语义标签信息。为了评估所提出的方法，我们在标准零样本数据集上进行了实验。实证结果表明，所提出的方法优于现有最先进的零样本学习方法。
Given the challenge of gathering labeled training data, zero-shot classification, which transfers information from observed classes to recognize unseen classes, has become increasingly popular in the computer vision community. Most existing zero-shot learning methods require a user to first provide a set of semantic visual attributes for each class as side information before applying a two-step prediction procedure that introduces an intermediate attribute prediction problem. In this paper, we propose a novel zero-shot classification approach that automatically learns label embeddings from the input data in a semi-supervised large-margin learning framework. The proposed framework jointly considers multi-class classification over all classes (observed and unseen) and tackles the target prediction problem directly without introducing intermediate prediction problems. It also has the capacity to incorporate semantic label information from different sources when available. To evaluate the proposed approach, we conduct experiments on standard zero-shot data sets. The empirical results show the proposed approach outperforms existing state-of-the-art zero-shot learning methods.