A Review of Generalized Zero-Shot Learning Methods

By Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, Xizhao Wang. 2020

575 citations recorded by the source corpus.

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. First, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations.

Subfields: Artificial Intelligence, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Subspace Regularizers for Few-Shot Class Incremental Learning (2021, background): “…information, a number of previous studies in vision and language have also investigated the effectiveness of directly learning a mapping from the space of semantic embeddings to the space of class weights (Das & Lee, 2019; Socher et al., 2013; Pourpanah et al., 2020; Romera-Paredes & Torr, 2015).”
Pre-trained Vision and Language Transformers are Few-Shot Incremental Learners (2024, background): “The utilization of language embeddings for effective learning of novel classes has been extensively explored in the context of generalized zero-shot learning [1, 25].”