High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning

arXiv:2404.04953v1 Announce Type: new
Abstract: Zero-shot learning(ZSL) aims to recognize new classes without prior exposure to their samples, relying on semantic knowledge from observed classes. However, current attention-based models may overlook the transferability of visual features and the distinctiveness of attribute localization when learning regional features in images. Additionally, they often overlook shared attributes among different objects. Highly discriminative attribute features are crucial for identifying and distinguishing unseen classes. To address these issues, we propose an innovative approach called High-Discriminative …

abstract arxiv attention cs.cv current feature features generalized however images knowledge localization prior regional samples semantic type visual zero-shot