  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.
