Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation

• URL: http://arxiv.org/abs/2112.12573v1
• Date: Thu, 23 Dec 2021 14:32:51 GMT
• Title: Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation
• Authors: Xiaojie Zhao, Yuming Shen, Shidong Wang, Haofeng Zhang
• Abstract summary: We propose a novel framework to boost Zero-Shot Learning (ZSL) by synthesizing diverse features. This method uses augmented semantic attributes to train the generative model, so as to simulate the real distribution of visual features. We evaluate the proposed model on four benchmark datasets, observing significant performance improvement against the state-of-the-art.
• Score: 21.72622601533585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent advance in deep generative models outlines a promising perspective in the realm of Zero-Shot Learning (ZSL). Most generative ZSL methods use category semantic attributes plus a Gaussian noise to generate visual features. After generating unseen samples, this family of approaches effectively transforms the ZSL problem into a supervised classification scheme. However, the existing models use a single semantic attribute, which contains the complete attribute information of the category. The generated data also carry the complete attribute information, but in reality, visual samples usually have limited attributes. Therefore, the generated data from attribute could have incomplete semantics. Based on this fact, we propose a novel framework to boost ZSL by synthesizing diverse features. This method uses augmented semantic attributes to train the generative model, so as to simulate the real distribution of visual features. We evaluate the proposed model on four benchmark datasets, observing significant performance improvement against the state-of-the-art.

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