Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2024

Abstract

Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes. To tackle this combined ZSL and OSR problem, we consider the case of “Zero-Shot Open-Set Recognition” (ZS-OSR), where a model is trained under the ZSL setting but it is required to accurately classify samples from the unseen classes while being able to reject samples from the unknown classes during inference. We perform large experiments on combining existing state-of-the-art ZSL and OSR models for the ZS-OSR task on four widely used datasets adapted from the ZSL task, and reveal that ZS-OSR is a non-trivial task as the simply combined solutions perform badly in distinguishing the unseen-class and unknown-class samples. We further introduce a novel approach specifically designed for ZS-OSR, in which our model learns to generate adversarial semantic embeddings of the unknown classes to train an unknowns-informed ZS-OSR classifier. Extensive empirical results show that our method 1) substantially outperforms the combined solutions in detecting the unknown classes while retaining the classification accuracy on the unseen classes and 2) achieves similar superiority under generalized ZS-OSR settings. Our code is available at https://github.com/lhrst/ASE.

Keywords

Open-Set Recognition (OSR), Zero-Shot Learning (ZSL), Zero-Shot Open-Set Recognition (ZS-OSR)

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

Pattern Recognition

Volume

149

First Page

1

Last Page

16

ISSN

0031-3203

Identifier

10.1016/j.patcog.2024.110258

Publisher

Elsevier

Copyright Owner and License

Authors-CC-BY

Additional URL

https://doi.org/10.1016/j.patcog.2024.110258

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