Class label autoencoder for zero-shot learning
This addresses the challenge of zero-shot learning for computer vision tasks by enabling more flexible semantic embeddings, though it appears incremental as it builds on existing projection-based methods.
The paper tackles the problem of zero-shot learning by proposing a class label autoencoder (CLA) that adapts to multi-semantic embedding spaces and improves classification by considering relationships between feature and semantic classes, achieving state-of-the-art results on four benchmark datasets (AwA, CUB, Dogs, ImNet-2).
Existing zero-shot learning (ZSL) methods usually learn a projection function between a feature space and a semantic embedding space(text or attribute space) in the training seen classes or testing unseen classes. However, the projection function cannot be used between the feature space and multi-semantic embedding spaces, which have the diversity characteristic for describing the different semantic information of the same class. To deal with this issue, we present a novel method to ZSL based on learning class label autoencoder (CLA). CLA can not only build a uniform framework for adapting to multi-semantic embedding spaces, but also construct the encoder-decoder mechanism for constraining the bidirectional projection between the feature space and the class label space. Moreover, CLA can jointly consider the relationship of feature classes and the relevance of the semantic classes for improving zero-shot classification. The CLA solution can provide both unseen class labels and the relation of the different classes representation(feature or semantic information) that can encode the intrinsic structure of classes. Extensive experiments demonstrate the CLA outperforms state-of-art methods on four benchmark datasets, which are AwA, CUB, Dogs and ImNet-2.