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Zero-shot Learning via Visual Feature Enhancement and Dual Classifier Learning for Image Recognition by Peng Zhao; Huihui Xue; Xia Ji; Huiting Liu; Li Han is a Computer Science article available to read on EtoBox.

What is Zero-shot Learning via Visual Feature Enhancement and Dual Classifier Learning for Image Recognition about?

Zero-shot image recognition attempts to simulate the zero-shot learning mechanism of humans and recognizes the images of novel classes. It is crucial to learn transferable knowledge from seen classes and generalize it to unseen classes for image recognition in zero-shot learning (ZSL). Most existing ZSL methods extract visual features with pretrained backbone networks and learn transferable knowledge with the extracted visual features. However, the backbone networks are not pretrained for a special task, and the extracted visual features usually contain some distractive information for the ZSL task, which causes some discriminative information to be ignored or weakened and degrades the quality of knowledge learned from seen classes. Moreover, since visual samples of unseen classes are not obtainable, domain shift is another challenging problem. In this paper, we propose visual feature enhancement to learn more discriminative visual features via a graph convolutional network (GCN) and an attention mechanism for improving the quality of the learned transferable knowledge. Different from previous works, we explore the correlations between different latent visual patterns of an image a

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Author

Peng Zhao; Huihui Xue; Xia Ji; Huiting Liu; Li Han

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)