Hybrid Discriminative Attribute-Object Embedding Network for Compositional Zero-Shot Learning

TL;DR

This paper introduces HDA-OE, a novel network for compositional zero-shot learning that synthesizes diverse attribute-object samples and embeds subclass information to better recognize unseen attribute-object combinations.

Contribution

The paper proposes a hybrid discriminative embedding network with attribute-driven data synthesis and subclass embedding modules, advancing compositional zero-shot learning capabilities.

Findings

Effective in recognizing unseen attribute-object pairs

Improves discriminative ability through subclass embedding

Validated on three benchmark datasets

Abstract

Compositional Zero-Shot Learning (CZSL) recognizes new combinations by learning from known attribute-object pairs. However, the main challenge of this task lies in the complex interactions between attributes and object visual representations, which lead to significant differences in images. In addition, the long-tail label distribution in the real world makes the recognition task more complicated. To address these problems, we propose a novel method, named Hybrid Discriminative Attribute-Object Embedding (HDA-OE) network. To increase the variability of training data, HDA-OE introduces an attribute-driven data synthesis (ADDS) module. ADDS generates new samples with diverse attribute labels by combining multiple attributes of the same object. By expanding the attribute space in the dataset, the model is encouraged to learn and distinguish subtle differences between attributes. To further improve the discriminative ability of the model, HDA-OE introduces the subclass-driven discriminative embedding (SDDE) module, which enhances the subclass discriminative ability of the encoding by embedding subclass information in a fine-grained manner, helping to capture the complex dependencies between attributes and object visual features. The proposed model has been evaluated on three benchmark datasets, and the results verify its effectiveness and reliability.