CapERA: Captioning Events in Aerial Videos

In this paper, we introduce the CapERA dataset, which upgrades the Event Recognition in Aerial Videos (ERA) dataset to aerial video captioning. The newly proposed dataset aims to advance visual–language-understanding tasks for UAV videos by providing each video with diverse textual descriptions. To build the dataset, 2864 aerial videos are manually annotated with a caption that includes information such as the main event, object, place, action, numbers, and time. More captions are automatically generated from the manual annotation to take into account as much as possible the variation in describing the same video. Furthermore, we propose a captioning model for the CapERA dataset to provide benchmark results for UAV video captioning. The proposed model is based on the encoder–decoder paradigm with two configurations to encode the video. The first configuration encodes the video frames independently by an image encoder. Then, a temporal attention module is added on the top to consider the temporal dynamics between features derived from the video frames. In the second configuration, we directly encode the input video using a video encoder that employs factorized space–time attention to capture the dependencies within and between the frames. For generating captions, a language decoder is utilized to autoregressively produce the captions from the visual tokens. The experimental results under different evaluation criteria show the challenges of generating captions from aerial videos. We expect that the introduction of CapERA will open interesting new research avenues for integrating natural language processing (NLP) with UAV video understandings

UAV Image Multi-Labeling with Data-Efficient Transformers

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model

Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization

In this paper, we present a new algorithm for cross-domain classification in aerial vehicle images based on generative adversarial networks (GANs). The proposed method, called Siamese-GAN, learns invariant feature representations for both labeled and unlabeled images coming from two different domains. To this end, we train in an adversarial manner a Siamese encoder–decoder architecture coupled with a discriminator network. The encoder–decoder network has the task of matching the distributions of both domains in a shared space regularized by the reconstruction ability, while the discriminator seeks to distinguish between them. After this phase, we feed the resulting encoded labeled and unlabeled features to another network composed of two fully-connected layers for training and classification, respectively. Experiments on several cross-domain datasets composed of extremely high resolution (EHR) images acquired by manned/unmanned aerial vehicles (MAV/UAV) over the cities of Vaihingen, Toronto, Potsdam, and Trento are reported and discussed

Visual Question Generation From Remote Sensing Images

Visual question generation (VQG) is a fundamental task in vision-language understanding that aims to generate relevant questions about the given input image. In this article, we propose a paragraph-based VQG approach for generating intelligent questions in natural language about remote sensing (RS) images. Specifically, our proposed framework consists of two transformer-based vision and language models. First, we employ a swin-transformer encoder to generate a multiscale representative visual feature from the image. Then, this feature is used as a prefix to guide a generative pretrained transformer-2 (GPT-2) decoder in generating multiple questions in the form of a paragraph to cover the abundant visual information contained in the RS scene. To train the model, the language decoder is fine-tuned on RS dataset to generate a set of relevant questions from the RS image. We evaluate our model on two visual question-answering (VQA) datasets in RS. In addition, we construct a new dataset termed TextRS-VQA for better evaluation for our VQG model. This dataset consists of questions completely annotated by humans which addresses the high redundancy of the questions in prior VQA datasets. Extensive experiments using several accuracy and diversity metrics demonstrate the effectiveness of our proposed VQG model in generating meaningful, valid, and diverse questions from RS images

Vision Transformers for Remote Sensing Image Classification

In this paper, we propose a remote-sensing scene-classification method based on vision transformers. These types of networks, which are now recognized as state-of-the-art models in natural language processing, do not rely on convolution layers as in standard convolutional neural networks (CNNs). Instead, they use multihead attention mechanisms as the main building block to derive long-range contextual relation between pixels in images. In a first step, the images under analysis are divided into patches, then converted to sequence by flattening and embedding. To keep information about the position, embedding position is added to these patches. Then, the resulting sequence is fed to several multihead attention layers for generating the final representation. At the classification stage, the first token sequence is fed to a softmax classification layer. To boost the classification performance, we explore several data augmentation strategies to generate additional data for training. Moreover, we show experimentally that we can compress the network by pruning half of the layers while keeping competing classification accuracies. Experimental results conducted on different remote-sensing image datasets demonstrate the promising capability of the model compared to state-of-the-art methods. Specifically, Vision Transformer obtains an average classification accuracy of 98.49%, 95.86%, 95.56% and 93.83% on Merced, AID, Optimal31 and NWPU datasets, respectively. While the compressed version obtained by removing half of the multihead attention layers yields 97.90%, 94.27%, 95.30% and 93.05%, respectively