Prior Knowledge about Attributes: Learning a More Effective Potential Space for Zero-Shot Recognition
This work improves zero-shot recognition for AI systems by handling attribute correlations, though it appears incremental as it builds on existing methods with a novel component.
The paper tackles the problem of zero-shot learning (ZSL) by addressing ignored attribute correlations that confuse classification, resulting in a model that outperforms state-of-the-art methods on benchmark datasets for both conventional and generalized ZSL.
Zero-shot learning (ZSL) aims to recognize unseen classes accurately by learning seen classes and known attributes, but correlations in attributes were ignored by previous study which lead to classification results confused. To solve this problem, we build an Attribute Correlation Potential Space Generation (ACPSG) model which uses a graph convolution network and attribute correlation to generate a more discriminating potential space. Combining potential discrimination space and user-defined attribute space, we can better classify unseen classes. Our approach outperforms some existing state-of-the-art methods on several benchmark datasets, whether it is conventional ZSL or generalized ZSL.