Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL). In order to alleviate these problems, we propose a novel generative ZSL method to learn more generalized features from multi-knowledge with continuously generated new semantics in semantic-to-visual embedding. In our approach, the proposed Multi-Knowledge Fusion Network (MKFNet) takes different semantic features from multi-knowledge as input, which enables more relevant semantic features to be trained for semantic-to-visual embedding, and finally generates more generalized visual features by adaptively fusing visual features from different knowledge domain. The proposed New Feature Generator (NFG) with adaptive genetic strategy is used to enrich semantic information on the one hand, and on the other hand it greatly improves the intersection of visual feature generated by MKFNet and unseen visual faetures. Empirically, we show that our approach can achieve significantly better performance compared to existing state-of-the-art methods on a large number of benchmarks for several ZSL tasks, including traditional ZSL, generalized ZSL and zero-shot retrieval.

中文翻译：

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广义零射击学习中新特征生成的多知识融合

由于存在语义上的不足和域转换问题，大多数现有的最新方法都无法为零热学习（ZSL）获得令人满意的结果。为了缓解这些问题，我们提出了一种新颖的ZSL生成方法，该方法可以从多知识中学习更多泛化的特征，并在语义到视觉的嵌入过程中不断生成新的语义。在我们的方法中，拟议的多知识融合网络（MKFNet）采用与多知识不同的语义特征作为输入，这使得可以训练更多相关的语义特征以进行语义到视觉的嵌入，并最终通过生成更通用的视觉特征。自适应融合来自不同知识领域的视觉特征。提出的具有自适应遗传策略的新特征生成器（NFG）一方面用于丰富语义信息，另一方面可以极大地改善MKFNet生成的视觉特征与看不见的视觉特征之间的交集。从经验上讲，我们显示，与针对多个ZSL任务（包括传统ZSL，广义ZSL和零镜头检索）的大量基准的现有最新技术相比，我们的方法可以实现明显更好的性能。