Zero-shot classification is a hot topic in computer vision and pattern recognition. Most zero-shot classification methods are based on the intermediate level representation of attributes to achieve knowledge transfer from the training classes to the unseen test classes. Recently, multitask learning (MTL) has been shown as one of state-of-the-art approaches for attribute learning and zero-shot classification. Aiming at the attribute relation learning, features shared by attributes learning and attribute heterogeneity, we propose a zero-shot classification based on multitask mixed attribute relations and attribute-specific features. First, considering the relationship between attribute–attribute and attribute–features, a second-order attribute relation and attribute-specific features learning model is constructed from training samples based on MTL. Second, second-order attribute relation is extended to high-order attribute relation and multiple attribute classifiers are learned. Finally, zero-shot classification is completed based on the maximum posterior probability. Experimental results on AWA and PubFig data sets show that the proposed method can yield more accurate attribute prediction and zero-shot classification compared with several multitask attribute learning methods.

中文翻译：

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基于多任务混合属性关系和属性特定特征的零样本分类

零样本分类是计算机视觉和模式识别中的热门话题。大多数零样本分类方法都是基于属性的中间级表示来实现从训练类到未知测试类的知识转移。最近，多任务学习 (MTL) 已被证明是属性学习和零样本分类的最先进方法之一。针对属性关系学习、属性学习共享的特征和属性异质性，我们提出了一种基于多任务混合属性关系和属性特定特征的零样本分类。首先，考虑属性-属性和属性-特征之间的关系，基于MTL从训练样本构建二阶属性关系和属性特定特征学习模型。其次，将二阶属性关系扩展为高阶属性关系，学习多个属性分类器。最后，根据最大后验概率完成零样本分类。在 AWA 和 PubFig 数据集上的实验结果表明，与几种多任务属性学习方法相比，所提出的方法可以产生更准确的属性预测和零样本分类。