  Generative models have achieved state-of-the-art performance for the
zero-shot learning problem, but they require re-training the classifier every
time a new object category is encountered. The traditional semantic embedding
approaches, though very elegant, usually do not perform at par with their
generative counterparts. In this work, we propose an unified framework termed
GenClass, which integrates the generator with the classifier for efficient
zero-shot learning, thus combining the representative power of the generative
approaches and the elegance of the embedding approaches. End-to-end training of
the unified framework not only eliminates the requirement of additional
classifier for new object categories as in the generative approaches, but also
facilitates the generation of more discriminative and useful features.
Extensive evaluation on three standard zero-shot object classification
datasets, namely AWA, CUB and SUN shows the effectiveness of the proposed
approach. The approach without any modification, also gives state-of-the-art
performance for zero-shot action classification, thus showing its
generalizability to other domains.
