Discriminative and noise-robust embedding for zero-shot learning.

Abstract

Zero-shot learning (ZSL) seeks to empower models with the ability to identify novel classes by transferring knowledge from previously encountered categories. Despite promising progress, most current ZSL methods rely heavily on pre-trained backbone networks for visual feature extraction. These features often contain distracting components that diminish the distinctiveness of critical information, thereby undermining knowledge transfer effectiveness. Moreover, existing methods typically overlook the impact of noisy or irrelevant details embedded in attribute representations. To address these limitations, we propose Discriminative and Noise-Robust Embedding (DNRE) for ZSL. It incorporates two major components: (1) a Channel Covariance Adaptive Enhancement (CCAE) module that captures higher-order dependencies and dynamically emphasizes informative channels to refine visual representations; and (2) a Dynamic Calibration Mechanism (DCM) that improves the alignment between attributes and local visual regions by suppressing noise and irrelevant signals. Comprehensive experiments on three widely-used ZSL benchmarks demonstrate that our method consistently surpasses state-of-the-art baselines, particularly in terms of accurate and robust attribute localization.