Dual Expert Distillation Network for Generalized Zero-Shot Learning

arXiv:2404.16348v1 Announce Type: new
Abstract: Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation. Existing studies resort to refining a uniform mapping function to align and correlate the sample regions and subattributes, ignoring two crucial issues: 1) the inherent asymmetry of attributes; and 2) the unutilized channel information. This paper addresses these issues by introducing a simple yet effective approach, dubbed Dual Expert Distillation Network (DEDN), where two experts are dedicated to coarse- and fine-grained visual-attribute …

abstract arxiv correlation cs.cv distillation expert function generalized information mapping modeling network progress sample studies type uniform via visual zero-shot