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检索条件"机构=Computer Vision and Robotics Laboratory"
644 条 记 录,以下是51-60 订阅
排序:
Hardware-in-the-loop simulation of a UAV autonomous landing algorithm implemented in SoC FPGA
arXiv
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arXiv 2022年
作者: Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland
This paper presents a system for hardware-in-the-loop (HiL) simulation of unmanned aerial vehicle (UAV) control algorithms implemented on a heterogeneous SoC FPGA computing platforms. The AirSim simulator running on a... 详细信息
来源: 评论
UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments
UnrealROX+: An Improved Tool for Acquiring Synthetic Data fr...
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2021 International Joint Conference on Neural Networks, IJCNN 2021
作者: Martinez-Gonzalez, Pablo Oprea, Sergiu Castro-Vargas, John Alejandro Garcia-Garcia, Alberto Orts-Escolano, Sergio Garcia-Rodriguez, Jose Vincze, Markus University of Alicante Department of Computer Technology Spain University of Alicante Department of Computer Science and Artificial Intelligence Spain Spain Tu Wien Vision for Robotics Laboratory Austria
Synthetic data generation has become essential in last years for feeding data-driven algorithms, which surpassed traditional techniques performance in almost every computer vision problem. Gathering and labelling the ... 详细信息
来源: 评论
Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation
TechRxiv
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TechRxiv 2022年
作者: Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland
Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate bot... 详细信息
来源: 评论
Towards real-time and energy efficient Siamese tracking - a hardware-software approach
TechRxiv
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TechRxiv 2022年
作者: Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland
Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve rea... 详细信息
来源: 评论
Playing cards and bidding calls detection for automatic registration of a duplicate bridge game
TechRxiv
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TechRxiv 2022年
作者: Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland
In this work, the implementation of a playing cards and bidding calls detection system for the automatic registration of a duplicate bridge game is presented. For this purpose, two YOLOv4 deep convolutional neural net... 详细信息
来源: 评论
Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation
arXiv
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arXiv 2022年
作者: Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland
Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate bot... 详细信息
来源: 评论
Towards real-time and energy efficient Siamese tracking - a hardware-software approach
arXiv
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arXiv 2022年
作者: Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland
Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve rea... 详细信息
来源: 评论
FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models
arXiv
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arXiv 2024年
作者: Tang, Chao Huang, Dehao Dong, Wenlong Xu, Ruinian Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States
Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous t... 详细信息
来源: 评论
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...
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Conference on computer vision and Pattern Recognition (CVPR)
作者: Weizhao He Yang Zhang Wei Zhuo Linlin Shen Jiaqi Yang Songhe Deng Liang Sun Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science University of Nottingham China
Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share si... 详细信息
来源: 评论
FeCAM: exploiting the heterogeneity of class distributions in exemplar-free continual learning  23
FeCAM: exploiting the heterogeneity of class distributions i...
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Proceedings of the 37th International Conference on Neural Information Processing Systems
作者: Dipam Goswami Yuyang Liu Bartłomiej Twardowski Joost van de Weijer Department of Computer Science Universitat Autònoma de Barcelona and Computer Vision Center Barcelona University of Chinese Academy of Sciences and State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences and Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Department of Computer Science Universitat Autònoma de Barcelona and Computer Vision Center Barcelona and IDEAS-NCBR
Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally le...
来源: 评论