# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 2 Related Work  
In this section, we provide an overview of various works related to the fields of few-shot and zero-shot learning. One of the earliest studies by [PERSON] et al. [2] introduces a method that relies on Generative Adversarial Networks (GANs). They utilize the conditional Wasserstein Generative Adversarial Network (WGAN) to produce image features. Given the challenges posed by RS images, which exhibit both inter-class similarity and intra-class diversity, they incorporate several constraints on the generator. These constraints include a classification loss to maintain discrimination between different classes, and a semantic regression component is employed to verify that the generated image features accurately reflect semantic attributes. Additionally, a class-prototype loss is introduced to encourage variation among the synthesized image features, preventing them from becoming excessively uniform. The work proposed by [6] introduces Class Adapting Principal Directions (CAPDs) and an alignment process to transfer information from seen to unseen classes, incorporates automatic selection of useful seen classes, adapts to few-shot learning, and generalizes CAPDs to improve performance by using different learning scenarios. In contrast, the methodology in [7] involves leveraging deep learning and meta-learning techniques to address the challenge of classifying RS scenes with limited labeled data. The preliminary results of this methodology have been reported using various RS scene datasets, indicating its potential effectiveness in few-shot learning. Another approach by [PERSON] et al. [8] focuses on scene image classification by constructing a new knowledge graph from scratch, specifically designed for RS data. They create a semantic representation of scene categories through representation learning. To achieve robust cross-modal matching between visual features and semantic representations, they propose a deep alignment network with a series of carefully designed optimization constraints. This network addresses both zero-shot and generalized zero-shot remote sensing image scene classification. [PERSON] et al. [9] introduce a VLM approach for RS scene classification, enhancing feature representations and enabling improved zero-shot classification accuracy across multiple benchmark datasets. The authors in [10] highlight the vital role of few-shot learning in addressing the data scarcity issue in RS image interpretation, a domain greatly benefiting from deep learning. The study offers a comprehensive review, categorizes two key few-shot learning methods, and outlines three primary applications, complete with datasets and evaluation criteria. In [11], they address the challenge of few-shot classification by introducing an approach based on the Earth Mover's Distance with spatial and channel attention modules. [PERSON] [12] presents a solution for generating synthetic training data for overhead object detection in satellite imagery, addressing challenges in acquiring real data. The proposed solution demonstrates effectiveness in zero-shot and few-shot learning scenarios and provides insights into design parameters while making its implementation and experimental imagery details available for wider use. [PERSON] et al. [13] present a semantic autoencoder with distance constraints designed for the task of zero-shot classification in RS scenes. They develop a semantic autoencoder for known scene classes, aligning the visual and semantic spaces. To enhance the discriminative capability of this semantic autoencoder, they introduce a discriminative distance metric constraint, aiming to minimize distances between encoded vectors of samples from the same class and maximize distances between samples from different classes. In a different study, [PERSON] and co-authors [14] suggest a zero-shot technique that relies on Sammon embedding and spectral clustering.