The main question we address in this paper is how to scale up visual
recognition of unseen classes, also known as zero-shot learning, to tens of
thousands of categories as in the ImageNet-21K benchmark. At this scale,
especially with many fine-grained categories included in ImageNet-21K, it is
critical to learn quality visual semantic representations that are
discriminative enough to recognize unseen classes and distinguish them from
seen ones. We propose a \emph{H}ierarchical \emph{G}raphical knowledge
\emph{R}epresentation framework for the confidence-based classification method,
dubbed as HGR-Net. Our experimental results demonstrate that HGR-Net can grasp
class inheritance relations by utilizing hierarchical conceptual knowledge. Our
method significantly outperformed all existing techniques, boosting the
performance by 7\% compared to the runner-up approach on the ImageNet-21K
benchmark. We show that HGR-Net is learning-efficient in few-shot scenarios. We
also analyzed our method on smaller datasets like ImageNet-21K-P, 2-hops and
3-hops, demonstrating its generalization ability. Our benchmark and code are
available at this https URL.

提出了一种基于层次图知识表示的置信度分类方法，命名为 HGR-Net，用于零样本学习和大规模图像识别，可以有效地识别未见过的类别并区分它们与已见过的类别，在 ImageNet-21K 基准测试中，性能比其他现有技术提升了 7%。