Towards Discriminative Feature Generation for Generalized Zero-Shot Learning

Abstract

Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen categories by establishing visual and semantic relations. Recently, generation-based methods that focus on synthesizing fictitious visual features from corresponding attributes have gained significant attention. However, these generated features often lack discriminative capabilities due to inadequate training of the generative model. To address this issue, we propose a novel Discriminative Enhanced Network (DENet) to harness the potential of the generative model by adapting the training features and imposing constraints on the generated features. Our approach incorporates three pivotal modules: (1) Before the generative network training, we implement a Pre-Tuning Module (PTM) to eliminate irrelevant background noise in the raw features extracted from a fixed CNN backbone. Therefore, PTM can provide tuned training features without redundant noise for generative model. (2) During the generative network training, we propose an Asymmetry Cross-authenticity Contrastive (AC2) loss to group visual features of the same category while repel features from different categories by optimizing a large number of sample pairs. Additionally, we incorporate intra-class and relation-specific inter-class boundaries within the AC2 loss to enrich sample diversity and preserve valid semantic information. (3) Also within the generative network training, a Dual-semantic Alignment Module (DAM) is designed to align visual features with both attributes and label embeddings, enabling the model to learn attribute-related information and discriminative extended semantics. Experiments on four standard benchmarks demonstrate that our approach learns more discriminative features and surpasses the existing methods.