# A Review of Generalized Zero-Shot Learning Methods

arXiv: https://arxiv.org/abs/2011.08641 · HTML: https://mlsanity.com/paper/2011.08641/

## Key facts

- Cited by 159 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 30 of 159)

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- [Frozen Language Model Helps ECG Zero-Shot Learning](https://mlsanity.com/paper/2303.12311/) (2023)
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- [A survey of multimodal deep generative models](https://mlsanity.com/paper/2207.02127/) (2022)
- [Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization](https://mlsanity.com/paper/2302.00275/) (2023)
- [Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners](https://mlsanity.com/paper/2404.02117/) (2024)
- [DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning](https://mlsanity.com/paper/2207.01328/) (2022)
- [ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing](https://mlsanity.com/paper/2303.02437/) (2023)
- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [A Review of Deep Learning for Video Captioning](https://mlsanity.com/paper/2304.11431/) (2023)
- [Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation](https://mlsanity.com/paper/2306.11087/) (2023)
- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances](https://mlsanity.com/paper/2409.11884/) (2024)
- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Language-Driven Anchors for Zero-Shot Adversarial Robustness](https://mlsanity.com/paper/2301.13096/) (2023)
- [Evolving Semantic Prototype Improves Generative Zero-Shot Learning](https://mlsanity.com/paper/2306.06931/) (2023)
- [Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition](https://mlsanity.com/paper/2404.07487/) (2024)
- [SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders](https://mlsanity.com/paper/2407.13460/) (2024)
- [Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer](https://mlsanity.com/paper/2405.19100/) (2024)
- [Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview](https://mlsanity.com/paper/2211.08352/) (2022)
- [COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing](https://mlsanity.com/paper/2402.16424/) (2024)
- [Cross-modal Representation Learning for Zero-shot Action Recognition](https://mlsanity.com/paper/2205.01657/) (2022)
- [Learning from Multiple Noisy Partial Labelers](https://mlsanity.com/paper/2106.04530/) (2021)
- [Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey](https://mlsanity.com/paper/2303.08557/) (2023)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Object Detection with Multimodal Large Vision-Language Models: An In-depth Review](https://mlsanity.com/paper/2508.19294/) (2025)
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- [Open-Vocabulary Animal Keypoint Detection with Semantic-feature Matching](https://mlsanity.com/paper/2310.05056/) (2023)

## Abstract (arXiv)

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. Firstly, 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.