Zero-shot learning (ZSL) aims to recognize unknown categories that are unavailable during
training. Recently, generative models have shown the potential to address this challenging …

With the rapid development of the Internet and the expanding scale of social media,
incomplete social event classification has increasingly become a challenging task. The key …

Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the
quality of synthesized features to recognize seen and unseen data. Therefore, learning a …

In the era of industrial artificial intelligence, grappling with data insufficiency remains a
formidable challenge that stands at the forefront of our progress. The embedding-aware …

Generalized zero-shot learning (GZSL) is an important research area in image computing,
video processing, multimedia understanding, and other visual computing tasks. GZSL …

面对人工标注大量样本费时费力, 一些稀有类别样本难于获取等问题, 零样本图像分类成为
计算机视觉领域的一个研究热点. 首先, 对零样本学习, 包括直推式零样本学习和归纳式零样本 …

Embedding-aware generative model (EAGM) addresses the data insufficiency problem for
zero-shot learning (ZSL) by constructing a generator between semantic and visual feature …

Current studies in Zero-Shot Learning for image classification use a weak Zero-Shot
condition by using curated attributes as semantics to guide the classification of unseen …