基于去冗余特征和语义关系约束的零样本属性识别

基于生成式的零样本识别方法在生成特征时受冗余信息和域偏移的影响,识别精度不佳.针对此问题,文中提出基于去冗余特征和语义关系约束的零样本属性识别方法.首先,将视觉特征映射到一个新的特征空间,通过互相关信息对视觉特征进行去冗余处理,在去除冗余视觉特征的同时保留类别的相关性,由于在识别过程中减少冗余信息的干扰,从而提高零样本识别的精度.然后,利用可见类和不可见类之间的语义关系建立知识迁移模型,并引入语义关系约束损失,约束知识迁移的过程,使生成器生成的视觉特征更能反映可见类和不可见类之间语义关系,缓解两者之间的域偏移问题.最后,引入循环一致性结构,使生成的伪特征更接近真实特征.在数据集上的实验证实文中方法提高零样本识别任务的精度,并具有较优的泛化性能.

Zero-Shot Attribute Recognition Based on De-redundancy Features and Semantic Relationship Constraint

The generative zero shot recognition method is affected by redundant information and domain shifting while generating features, and thus its recognition accuracy is poor. To deal with the problem, a zero shot attribute recognition method based on de-redundant features and semantic relationship constraint is proposed. Firstly, the visual features are mapped to a new feature space, and the visual features are de-redundant via cross-correlation information. The redundant visual features are removed with the correlation of the categories preserved. The accuracy of zero shot recognition is improved due to the reduction of redundant information interference in the recognition process. Then, a knowledge transfer model is established using the semantic relationship between the seen and unseen classes, and the loss of semantic relationship is introduced to constrain the process of knowledge transfer. Consequently, the semantic relationship between the seen and unseen classes is reflected better by the visual features generated by the generator ,and the problem of domain shifting between them is alleviated as well. Finally, the cycle consistency structure is introduced to make the generated pseudo-features closer to the real features. Experiments on datasets show that the proposed method improves the accuracy of zero shot recognition tasks with better generalization performance.