活动简介
Recent advances in deep learning techniques have made impressive progress in many areas of computer vision, including classification, detection, and segmentation. While all of these areas are relevant to robotics applications, robotics also presents many unique challenges which require new approaches. Challenges include the need for real-time analysis, the need for accurate 3d understanding of scenes, and the difficulty of doing experiments at scale. There are also opportunities which robotics brings to computer vision, for example, the ability to use depth sensors, to control where the camera is looking, and to provide a data source for "grounded" learning of concepts, reducing the need for manual labeling. We will consider work related to deep learning techniques in computer vision applied to a broad range of robotic devices, from self driving cars to drones to bipedal robots.
征稿信息
重要日期
征稿范围
We invite contributions (2 page extended abstracts) related to:
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Deep learning for robotic vision
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Other computer vision techniques applied to robotics problems
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DNN based object recognition, detection and segmentation for robotics
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End-to-end perception algorithms
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Real-time algorithms for robotics perception
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Vision-based Simultaneous Localization and Mapping (SLAM)
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3D Scene understanding
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Deep learning in navigation and autonomous driving
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Deep learning in human-robot interaction
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Lifelong deep learning in robotics
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Perception algorithms deployed on various robotic systems
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Reliable confidence measures for deep classifiers
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Deep learning for embedded systems and platforms with limited computational power
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Deep learning for smart environments
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Deep learning applications for the visually impaired and for the ageing society
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Active perception
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Semi-supervised and self-supervised learning for robotics
全部留言
重要日期
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07月21日
2017
会议日期
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04月07日 2017
摘要截稿日期
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05月03日 2017
摘要录用通知日期
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07月21日 2017
注册截止日期
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