# UAV Image Multi-Labeling with Data-Efficient Transformers
## 1 Introduction  
Airborne cameras and unmanned aerial vehicle (UAVs) sensors have been greatly advanced in the past decade, providing a rich source of high-resolution data for researchers and practitioners. Images collected by these platforms have been exploited in numerous tasks such as change detection and scene classification [1]. Among all the tasks, scene classification, in particular, has been extensively studied for UAV imagery [2]. It has the goal of associating an image with a unique semantic label describing the most predominant object in the image. Typically, UAV images are characterized by their complex visual contents. That is, one image can contain several objects and can simultaneously be associated with multiple class labels. This makes one label inadequate to describe a scene with multiple objects. To tackle this, scene understanding has been approached through several methodologies that give a more comprehensive description of the image content such as, image segmentation [3], which aims to classify every pixel in the aerial image, and object detection [4], which localizes and identifies different objects existing in the scene. Unlike scene classification, the ground truth for these methodologies demands expensive and time-consuming annotation. On the contrary, annotation during scene classification requires labels at the scene level only. For these reasons, the interest in multi-label classification, which aims to assign an image with multiple semantic labels, is increasing in the community [5; 6; 7; 8; 9; 10]. It is an essential step to provide a better understanding of the scene. Furthermore, many real-world applications can be implicitly formulated as multi-label classification problems such as image retrieval [5], object detection [21; 22], and semantics segmentation [23]. There are two classical approaches to deal with multi-label classification [10]. The first and simplest approach is the transformation, which formulates the multi-label classification as a multiple binary classification problem. In this approach, a set of classifiers are trained independently for each class against all the other classes. One of the major drawbacks of this approach is that it does not scale well when the number of classes grows large. The second approach is algorithm adaptation, in which multi-class classifiers are modified so they can be applied to multi-label problems. Various methods have been developed to perform multi-label classification for remote sensing, with few works targeting the UAV imagery. Early works on the field have achieved limited performance due to the utilization of hand-crafted features, which have limited ability to model the high-level discriminative features necessary for multi-label classification. In contrast, deep learning architectures, especially the Convolutional Neural Networks (CNNs), have made significant progress in many vision tasks and have proved to be an effective tool for extracting high-level features. As a result, scene classification literature has adopted CNNs in many multi-label scene classification works [7]. However, CNN models were originally designed for single-label classification, and they cannot completely leverage the correlations among multiple classes. To overcome this limitation, recent deep-learning methods proposed for multi-label classification tried to integrate other modules to model the potential correlations between labels. Some works proposed to exploit the semantic relationships using sequential or graph models. They use CNNs for feature extraction and then utilize Recurrent Neural Networks (RNN) [14] or graph models [7; 18] to handle these correlations. This is usually achieved with the help of visual attention mechanism to localize regions that are useful for making accurate predictions. Meanwhile, a new type of deep-learning model known as transformers has been developed for natural language processing (NLP) and has started to gain some popularity in computer vision [24] and remote sensing communities [25; 26].