Balancing Cross-Modal Attention for Generalized Zero-Shot Learning

Recent advances in Generalized Zero-Shot Learning have demonstrated promising results by leveraging cross-modal attention mechanisms to model the nuanced relationships between semantic attributes and visual regions. However, we find that inherent attribute imbalance leads to significant attention disparities, i.e., those attributes with less sample weights often exhibit lower attention confidence and poorer localization accuracy. This attention illusion obviously leads to potential degraded or even counterproductive contributions to category determination. Conventional rebalancing approaches like resampling or reweighting are ineffective for attention calibration due to the complex interdependencies among attributes in one object and the absence of region-level annotation guidance. In this paper, we propose a novel coarse-to-fine framework termed Hierarchical Progressive Attention Network (HPAN), which leverages latent attention consistency across attributes to rectify the focus deviation of minor attributes. HPAN comprises two synergistic components, Super-Attribute Guided Generable Attention (SGGA) and Rebalanced Attribute Attention Calibration (RAAC). SGGA employs super-attributes to establish unified attention regions for both major and minor attributes, subsequently propagating these attention masks to RAAC for calibration. RAAC specializes in capturing fine-grained attribute-region interaction relations, with its output attention masks serving as supervisory signals to iteratively optimize the coarse attention of SGGA. Extensive experiments on three benchmark datasets verify the effectiveness of the proposed method. Code is available at github.com/zjrao/HPAN.