# Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

arXiv: https://arxiv.org/abs/2004.14143 · HTML: https://mlsanity.com/paper/2004.14143/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 20 of 20)

- [Zero-shot Object Counting](https://mlsanity.com/paper/2303.02001/) (2023)
- [Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/2108.06230/) (2021)
- [Generating Representative Samples for Few-Shot Classification](https://mlsanity.com/paper/2205.02918/) (2022)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection](https://mlsanity.com/paper/2304.05096/) (2023)
- [A Comparative Review of Recent Few-Shot Object Detection Algorithms](https://mlsanity.com/paper/2111.00201/) (2021)
- [Zero-Shot Object Counting with Language-Vision Models](https://mlsanity.com/paper/2309.13097/) (2023)
- [Adaptive Masking Enhances Visual Grounding](https://mlsanity.com/paper/2410.03161/) (2024)
- [Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs](https://mlsanity.com/paper/2107.06563/) (2021)
- [Updater-Extractor Architecture for Inductive World State Representations](https://mlsanity.com/paper/2104.05500/) (2021)
- [Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning](https://mlsanity.com/paper/2309.01390/) (2023)
- [DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning](https://mlsanity.com/paper/2301.00236/) (2022)
- [HazardNet: Road Debris Detection by Augmentation of Synthetic Models](https://mlsanity.com/paper/2303.07547/) (2023)
- [Zero-sample surface defect detection and classification based on semantic feedback neural network](https://mlsanity.com/paper/2106.07959/) (2021)
- [SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2312.13100/) (2023)
- [Direct side information learning for zero-shot regression](https://mlsanity.com/paper/2402.01264/) (2024)
- [MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder](https://mlsanity.com/paper/2403.04626/) (2024)
- [RURA-Net: A general disease diagnosis method based on Zero-Shot Learning](https://mlsanity.com/paper/2503.00052/) (2025)
- [Adversarial Robustness in Zero-Shot Learning:An Empirical Study on Class and Concept-Level Vulnerabilities](https://mlsanity.com/paper/2512.18651/) (2025)

## Abstract (arXiv)

The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. We introduce a novel and broaden solution called Few/one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. We then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection/recognition systems using ZSL.