As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data. To address this problem, ZSL is usually carried out in the following two aspects: 1) capturing the domain distribution connections between seen classes data and unseen classes data and 2) modeling the semantic interactions between the image feature space and the label embedding space. Motivated by these observations, we propose a bidirectional mapping-based semantic relationship modeling scheme that seeks for cross-modal knowledge transfer by simultaneously projecting the image features and label embeddings into a common latent space. Namely, we have a bidirectional connection relationship that takes place from the image feature space to the latent space as well as from the label embedding space to the latent space. To deal with the domain shift problem, we further present a transductive learning approach that formulates the class prediction problem in an iterative refining process, where the object classification capacity is progressively reinforced through bootstrapping-based model updating over highly reliable instances. Experimental results on four benchmark datasets (animal with attribute, Caltech-UCSD Bird2011, aPascal-aYahoo, and SUN) demonstrate the effectiveness of the proposed approach against the state-of-the-art approaches.

中文翻译：

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自训练词典方法的转导零射击学习

作为计算机视觉中一个重要且具有挑战性的问题，零镜头学习（ZSL）旨在自动识别来自看不见的对象类别的实例，而无需训练数据。为了解决这个问题，ZSL通常在以下两个方面进行：1）捕获可见的类数据和不可见的类数据之间的域分布连接，以及2）对图像特征空间和标签嵌入空间之间的语义交互进行建模。基于这些观察，我们提出了一种基于双向映射的语义关系建模方案，该方案通过将图像特征和标签嵌入同时投影到一个共同的潜在空间中来寻求交叉模式的知识转移。即 我们具有从图像特征空间到潜在空间以及从标签嵌入空间到潜在空间的双向连接关系。为了解决域转移问题，我们进一步提出了一种跨语言学习方法，该方法在迭代细化过程中制定了类预测问题，其中通过在高度可靠的实例上基于自举的模型更新来逐步增强对象分类能力。在四个基准数据集（具有属性的动物，Caltech-UCSD Bird2011，aPascal-aYahoo和SUN）上的实验结果证明了该方法相对于最新方法的有效性。我们进一步提出了一种归纳式学习方法，该方法在迭代提炼过程中制定了类预测问题，其中通过在高度可靠的实例上基于自举的模型更新来逐步增强对象分类能力。在四个基准数据集（具有属性的动物，Caltech-UCSD Bird2011，aPascal-aYahoo和SUN）上的实验结果证明了该方法相对于最新方法的有效性。我们进一步提出了一种归纳式学习方法，该方法在迭代提炼过程中制定了类预测问题，其中通过在高度可靠的实例上基于自举的模型更新来逐步增强对象分类能力。在四个基准数据集（具有属性的动物，Caltech-UCSD Bird2011，aPascal-aYahoo和SUN）上的实验结果证明了该方法相对于最新方法的有效性。