Current UAV-recorded datasets are mostly limited to action recognition and object tracking, whereas the gesture signals datasets were mostly recorded in indoor spaces. Currently, there is no outdoor recorded public video dataset for UAV commanding ...

Moving object detection is an imperative task in computer vision, where it is primarily used for surveillance applications. With the increasing availability of low-altitude aerial vehicles, new challenges for moving object detection have surfaced, ...

On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms. We propose a computationally inexpensive detection network for vehicle detection in UAV imagery which we call ShuffleDet. In order to ...

Vision based algorithms became popular for state estimation and subsequent (local) control of mobile robots. Currently a large variety of such algorithms exists and their performance is often characterized through their drift relative to the total ...

Despite the emergence of interpretable machine learning as a distinct area of research, the role and possible uses of interpretability in digital art history are still unclear. Focusing on feature visualization as the most common technical ...

We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of our multiple-instance learning method, enables one to learn new ...

Automatic art analysis has been mostly focused on classifying artworks into different artistic styles. However, understanding an artistic representation involves more complex processes, such as identifying the elements in the scene or recognizing ...

To unveil the mystery of the exquisitely rendered materials in Dutch 17^th century paintings, we need to understand the pictorial procedures of this period. We focused on the Dutch master Jan de Heem, known for his highly convincing still-lifes. We ...

Today, computer vision is broadly implemented and operates in the background of many systems. For users of these technologies, there is often no visual feedback, making it hard to understand the mechanisms that drive it. When computer vision is ...

Environmental perception for autonomous aerial vehicles is a rising field. Recent years have shown a strong increase of performance in terms of accuracy and efficiency with the aid of convolutional neural networks. Thus, the community has ...

The intersection between computer vision and art history has resulted in new ways of seeing, engaging and analyzing digital images. Innovative methods and tools have assisted with the evaluation of large datasets, performing tasks such as ...

In this paper we investigate whether Deep Convolutional Neural Networks (DCNNs), which have obtained state of the art results on the ImageNet challenge, are able to perform equally well on three different art classification problems. In particular,...

We study the problem of georeferencing artistic historical maps. Since they were primarily conceived as work of art more than an accurate cartographic tool, the common warping approaches implemented in Geographic Application Systems (GIS) usually ...

State of the art Computer Vision techniques exploit the availability of large-scale datasets, most of which consist of images captured from the world as it is. This brings to an incompatibility between such methods and digital data from the ...

The purpose of this work is to explore self-supervised learning (SSL) strategy to capture a better feature with spatiotemporal 3D convolution. Although one of the next frontier in video recognition must be spatiotemporal 3D CNN, the convergence of ...${\mathrm{}}^{\mathrm{}}$$\mathrm{}$

In this paper, we propose a novel human-centric visual relation segmentation method based on Mask R-CNN model and VTransE model. We first retain the Mask R-CNN model, and segment both human and object instances. Because Mask R-CNN may omit some ...

In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accuracy on the Person in Context (PIC) dataset. We propose to group ...

Recent advances in computer vision have achieved remarkable performance improvements. These technologies mainly focus on recognition of visible targets. However, there are many invisible targets in blind spots in real situations. Humans may be ...

We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose ...