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检索条件"机构=KAIST Robotics and Computer Vision Lab."
67 条 记 录,以下是1-10 订阅
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Optical flow estimation from a single motion-blurred image
arXiv
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arXiv 2021年
作者: Argaw, Dawit Mureja Kim, Junsik Rameau, Francois Cho, Jae Won Kweon, In So KAIST Robotics and Computer Vision Lab. Daejeon Korea Republic of
In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in fundamental computer vision problems... 详细信息
来源: 评论
Restoration of video frames from a single blurred image with motion understanding
arXiv
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arXiv 2021年
作者: Argaw, Dawit Mureja Kim, Junsik Rameau, Francois Zhang, Chaoning Kweon, In So Kaist Robotics and Computer Vision Lab. Daejeon Korea Republic of
We propose a novel framework to generate clean video frames from a single motion-blurred image. While a broad range of literature focuses on recovering a single image from a blurred image, in this work, we tackle a mo... 详细信息
来源: 评论
Towards efficient human-robot cooperation for socially-aware robot navigation in human-populated environments: the SNAPE framework
Towards efficient human-robot cooperation for socially-aware...
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IEEE International Conference on robotics and Automation (ICRA)
作者: A. Vega-Magro R. Gondkar L.J. Manso P. Núñez Robotics and Artificial Vision Lab. RoboLab Group University of Extremadura Spain Pune Institute of Computer Technology Pune University India College of Engineering and Physical Sciences Aston University Birmingham UK
It is widely accepted that in the future, robots will cooperate with humans in everyday tasks. Robots interacting with humans will require social awareness when performing their tasks which will require navigation. Wh... 详细信息
来源: 评论
SESAME: Semantic editing of scenes by adding, manipulating or erasing objects
arXiv
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arXiv 2020年
作者: Ntavelis, Evangelos Romero, Andrés Kastanis, Iason van Gool, Luc Timofte, Radu Computer Vision Lab. ETH Zurich Switzerland Robotics and Machine Learning CSEM SA Switzerland PSI ESAT KU Leuven Belgium
Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are... 详细信息
来源: 评论
Context-transformer: Tackling object confusion for few-shot detection
arXiv
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arXiv 2020年
作者: Yang, Ze Wang, Yali Chen, Xianyu Liu, Jianzhuang Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Huawei Noah’s Ark Lab. SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society
Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are availab.e for training detectors. A popular approach to handle this problem is transfer learning, i.e.,... 详细信息
来源: 评论
Learning to super resolve intensity images from events
arXiv
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arXiv 2019年
作者: Mostafavi, Sayed Mohammad I. Choi, Jonghyun Yoon, Kuk-Jin Computer Vision Lab. GIST Korea Republic of Visual Intelligence Lab. KAIST Korea Republic of
An event camera detects per-pixel intensity difference and produces asynchronous event stream with low latency, high dynamic sensing range, and low power consumption. As a trade-off, the event camera has low spatial r... 详细信息
来源: 评论
OTE: Optimal Trustworthy EdgeAI solutions for smart cities
OTE: Optimal Trustworthy EdgeAI solutions for smart cities
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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)
作者: Vasileios Mygdalis Lorenzo Carnevale Jose Ramiro Martí nez-De-Dios Dmitriy Shutin Giovanni Aiello Massimo Villari Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Mathematical and Computer Science Physics and Hearth Sciences University of Messina Messina Italy Gruppo Nazionale per il Calcolo Scientifico (GNCS) Istituto Nazionale di Alta Matematica (INdAM) &#x201C F. Severi&#x201D Rome Italy Robotics Vision and Control Group University of Seville Seville Spain Institute of Communications and Navigation German Aerospace Center (DLR) Wessling Germany Research and Development Lab. Engineering Ingegneria Informatica S.p.A Rome Italy
This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing a... 详细信息
来源: 评论
DISC: A Large-scale Virtual Dataset for Simulating Disaster Scenarios
DISC: A Large-scale Virtual Dataset for Simulating Disaster ...
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
作者: Hae-Gon Jeon Sunghoon Im Byeong-Uk Lee Dong-Geol Choi Martial Hebert In So Kweon The Robotics Institute Carnegie Mellon University Pittsburgh PA USA The Robotics and Computer Vision Lab. KAIST Daejeon Republic of Korea Department of Information and Communication Engineering Hanbat National University Daejeon Republic of Korea
In this paper, we present the first large-scale synthetic dataset for visual perception in disaster scenarios, and analyze state-of-the-art methods for multiple computer vision tasks with reference baselines. We simul...
来源: 评论
TTPP: Temporal transformer with progressive prediction for efficient action anticipation
arXiv
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arXiv 2020年
作者: Wang, Wen Peng, Xiaojiang Su, Yanzhou Qiao, Yu Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society
Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states,... 详细信息
来源: 评论
Learning to specialize with knowledge distillation for visual question answering  18
Learning to specialize with knowledge distillation for visua...
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Proceedings of the 32nd International Conference on Neural Information Processing Systems
作者: Jonghwan Mun Kimin Lee Jinwoo Shin Bohyung Han Computer Vision Lab. POSTECH Pohang Korea and Computer Vision Lab. ASRI Seoul National University Seoul Korea Algorithmic Intelligence Lab. KAIST Daejeon Korea Computer Vision Lab. ASRI Seoul National University Seoul Korea
Visual Question Answering (VQA) is a notoriously challenging problem because it involves various heterogeneous tasks defined by questions within a unified framework. Learning specialized models for individual types of...
来源: 评论