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检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"
5506 条 记 录,以下是451-460 订阅
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DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset for Real-World Outdoor Scenarios
DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Bolten, Tobias Pohle-Froehlich, Regina Toennies, Klaus D. Hsch Niederrhein Inst Pattern Recognit Krefeld Germany Univ Magdeburg Dept Simulat & Graph Magdeburg Germany
Neuromorphic vision sensors are biologically inspired devices which differ fundamentally from well known frame-based sensors. Even though developments in this research area are increasing, applications that rely entir... 详细信息
来源: 评论
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
Good at captioning, bad at counting: Benchmarking GPT-4V on ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Chenhui Wang, Sherrie MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA
Large vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natur... 详细信息
来源: 评论
NTIRE 2022 Challenge on Learning the Super-Resolution Space
NTIRE 2022 Challenge on Learning the Super-Resolution Space
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Lugmayr, Andreas Danelljan, Martin Timofte, Radu Kim, Kang-wook Kim, Younggeun Lee, Jae-young Li, Zechao Pan, Jinshan Shim, Dongseok Song, Ki-Ung Tang, Jinhui Wang, Cong Zhao, Zhihao Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland
This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the s... 详细信息
来源: 评论
From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains
From Evaluation to Verification: Towards Task-oriented Relev...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Lyssenko, Maria Gladisch, Christoph Heinzemann, Christian Woehrle, Matthias Triebel, Rudolph Robert Bosch GmbH Corp Res Robert Bosch Campus 1 D-71272 Renningen Germany Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany German Aerosp Ctr DLR Munchener Str 20 D-82234 Wessling Germany
Whenever a visual perception system is employed in safety-critical applications such as automated driving, a thorough, task-oriented experimental evaluation is necessary to guarantee safe system behavior. While most s... 详细信息
来源: 评论
DIY Human Action dataset Generation  31
DIY Human Action dataset Generation
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Khodabandeh, Mehran Joze, Hamid Reza Vaezi Zharkov, Ilya Pradeep, Vivek Simon Fraser Univ Burnaby BC Canada Microsoft Redmond WA USA
The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in t... 详细信息
来源: 评论
IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation  31
IR2VI: Enhanced Night Environmental Perception by Unsupervis...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liu, Shuo John, Vijay Blasch, Erik Liu, Zheng Huang, Ying Univ British Columbia Vancouver BC Canada Toyota Inst Technol Nagoya Aichi Japan Air Force Res Lab Wright Patterson AFB OH USA Chongqing Univ Posts & Telecommun Chongqing Peoples R China
Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infrared-to-visual (IR2VI) algorithm, a novel un... 详细信息
来源: 评论
Brain-inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform
Brain-inspired Classroom Occupancy Monitoring on a Low-Power...
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27th IEEE conference on computer vision and pattern recognition (CVPR)
作者: Conti, Francesco Pullini, Antonio Benini, Luca Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland
Brain-inspired computer vision (BICV) has evolved rapidly in recent years and it is now competitive with traditional CV approaches. However, most of BICV algorithms have been developed on high power-and-performance pl... 详细信息
来源: 评论
Ex-Model: Continual Learning from a Stream of Trained Models
Ex-Model: Continual Learning from a Stream of Trained Models
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Carta, Antonio Cossu, Andrea Lomonaco, Vincenzo Bacciu, Davide Univ Pisa Pisa Italy Scuola Normale Super Pisa Pisa Italy
Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and... 详细信息
来源: 评论
Is synthetic voice detection research going into the right direction?
Is synthetic voice detection research going into the right d...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Borzi, Stefano Giudice, Oliver Stanco, Filippo Allegra, Dario Univ Catania Dept Math & Comp Sci Catania Italy Banca Italia IT Dept Appl Res Team Rome Italy
Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evol... 详细信息
来源: 评论
Less is More: Proxy Datasets in NAS approaches
Less is More: Proxy Datasets in NAS approaches
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Moser, Brian Raue, Federico Hees, Joern Dengel, Andreas German Res Ctr Artificial Intelligence DFKI Darmstadt Germany TU Kaiserslautern Kaiserslautern Germany
Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the d... 详细信息
来源: 评论