Title: Zero-Shot Learning by Generating Pseudo Feature Representations,
Abstract: Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel
classes without any training instances. In this paper we present a simple but
high-performance ZSL approach by generating pseudo feature representations
(GPFR). Given the dataset of seen classes and side information of unseen
classes (e.g. attributes), we synthesize feature-level pseudo representations
for novel concepts, which allows us access to the formulation of unseen class
predictor. Firstly we design a Joint Attribute Feature Extractor (JAFE) to
acquire understandings about attributes, then construct a cognitive repository
of attributes filtered by confidence margins, and finally generate pseudo
feature representations using a probability based sampling strategy to
facilitate subsequent training process of class predictor. We demonstrate the
effectiveness in ZSL settings and the extensibility in supervised recognition
scenario of our method on a synthetic colored MNIST dataset (C-MNIST). For
several popular ZSL benchmark datasets, our approach also shows compelling
results on zero-shot recognition task, especially leading to tremendous
improvement to state-of-the-art mAP on zero-shot retrieval task.