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- ArticleJanuary 2019
UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 117–128https://doi.org/10.1007/978-3-030-11012-3_9AbstractCurrent 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 ...
- ArticleJanuary 2019
Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 100–116https://doi.org/10.1007/978-3-030-11012-3_8AbstractMoving 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, ...
- ArticleJanuary 2019
ShuffleDet: Real-Time Vehicle Detection Network in On-Board Embedded UAV Imagery
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 88–99https://doi.org/10.1007/978-3-030-11012-3_7AbstractOn-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 ...
- ArticleJanuary 2019
Metrics for Real-Time Mono-VSLAM Evaluation Including IMU Induced Drift with Application to UAV Flight
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 73–87https://doi.org/10.1007/978-3-030-11012-3_6AbstractVision 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 ...
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- ArticleJanuary 2019
Images of Image Machines. Visual Interpretability in Computer Vision for Art
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 710–715https://doi.org/10.1007/978-3-030-11012-3_54AbstractDespite 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 ...
- ArticleJanuary 2019
Weakly Supervised Object Detection in Artworks
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 692–709https://doi.org/10.1007/978-3-030-11012-3_53AbstractWe 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 ...
- ArticleJanuary 2019
How to Read Paintings: Semantic Art Understanding with Multi-modal Retrieval
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 676–691https://doi.org/10.1007/978-3-030-11012-3_52AbstractAutomatic 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 ...
- ArticleJanuary 2019
A Digital Tool to Understand the Pictorial Procedures of 17 Century Realism
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 671–675https://doi.org/10.1007/978-3-030-11012-3_51AbstractTo unveil the mystery of the exquisitely rendered materials in Dutch 17th 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 ...
- ArticleJanuary 2019
Seeing the World Through Machinic Eyes: Reflections on Computer Vision in the Arts
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 653–670https://doi.org/10.1007/978-3-030-11012-3_50AbstractToday, 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 ...
- ArticleJanuary 2019
Aerial GANeration: Towards Realistic Data Augmentation Using Conditional GANs
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 59–72https://doi.org/10.1007/978-3-030-11012-3_5AbstractEnvironmental 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 ...
- ArticleJanuary 2019
Reflecting on How Artworks Are Processed and Analyzed by Computer Vision
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 647–652https://doi.org/10.1007/978-3-030-11012-3_49AbstractThe 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 ...
- ArticleJanuary 2019
Deep Transfer Learning for Art Classification Problems
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 631–646https://doi.org/10.1007/978-3-030-11012-3_48AbstractIn 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,...
- ArticleJanuary 2019
Saliency-Driven Variational Retargeting for Historical Maps
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 617–630https://doi.org/10.1007/978-3-030-11012-3_47AbstractWe 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 ...
- ArticleJanuary 2019
What Was Monet Seeing While Painting? Translating Artworks to Photo-Realistic Images
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 601–616https://doi.org/10.1007/978-3-030-11012-3_46AbstractState 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 ...
- ArticleJanuary 2019
- ArticleJanuary 2019
Human-Centric Visual Relation Segmentation Using Mask R-CNN and VTransE
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 582–589https://doi.org/10.1007/978-3-030-11012-3_44AbstractIn 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 ...
- ArticleJanuary 2019
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 571–581https://doi.org/10.1007/978-3-030-11012-3_43AbstractIn 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 ...
- ArticleJanuary 2019
Recognizing People in Blind Spots Based on Surrounding Behavior
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 562–570https://doi.org/10.1007/978-3-030-11012-3_42AbstractRecent 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 ...
- ArticleJanuary 2019
Semantically Selective Augmentation for Deep Compact Person Re-Identification
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 551–561https://doi.org/10.1007/978-3-030-11012-3_41AbstractWe 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 ...