Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning

arXiv:2404.07713v1 Announce Type: cross
Abstract: Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose …

abstract arxiv cnn cs.cv cs.lg extract features however information interactions knowledge learn network ones semantic transfer transformer type vision visual vit zero-shot