Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition

Abstract

Zero-shot learning (ZSL) aims to recognize objects of novel classes without any training samples of specific classes, which is achieved by exploiting the semantic information and auxiliary datasets. Recently most ZSL approaches focus on learning visual-semantic embeddings to transfer knowledge from the auxiliary datasets to the novel classes. However, few works study whether the semantic information is discriminative or not for the recognition task. To tackle such problem, we propose a coupled dictionary learning approach to align the visual-semantic structures using the class prototypes, where the discriminative information lying in the visual space is utilized to improve the less discriminative semantic space. Then, zero-shot recognition can be performed in different spaces by the simple nearest neighbor approach using the learned class prototypes. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach.

arXiv commentTo appear in ECCV 2018

References in corpus (8)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
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Cited by in corpus (3)

• Visual Space Optimization for Zero-shot Learning
• Domain segmentation and adjustment for generalized zero-shot learning
• Learning Clusterable Visual Features for Zero-Shot Recognition