Hybrid Discriminative Attribute-Object Embedding Network for Compositional Zero-Shot Learning
This work addresses compositional zero-shot learning for computer vision, which is incremental as it builds on existing methods with novel modules for data synthesis and discriminative embedding.
The paper tackled the problem of recognizing new attribute-object combinations in compositional zero-shot learning by addressing complex interactions and long-tail distributions, proposing a Hybrid Discriminative Attribute-Object Embedding network that achieved verified effectiveness on three benchmark datasets.
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.