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pro vyhledávání: '"Zhang, Jimuyang"'
We propose XVO, a semi-supervised learning method for training generalized monocular Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and settings. In contrast to standard monocular VO approaches which often stud
Externí odkaz:
http://arxiv.org/abs/2309.16772
We propose a novel knowledge distillation framework for effectively teaching a sensorimotor student agent to drive from the supervision of a privileged teacher agent. Current distillation for sensorimotor agents methods tend to result in suboptimal l
Externí odkaz:
http://arxiv.org/abs/2306.10014
Effectively utilizing the vast amounts of ego-centric navigation data that is freely available on the internet can advance generalized intelligent systems, i.e., to robustly scale across perspectives, platforms, environmental conditions, scenarios, a
Externí odkaz:
http://arxiv.org/abs/2204.10320
Autor:
Zhang, Jimuyang, Ohn-Bar, Eshed
When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed. In contrast, existing techniques for learning to drive preclude such a po
Externí odkaz:
http://arxiv.org/abs/2106.05966
Autor:
Zhang, Jimuyang, Zhou, Sanping, Chang, Xin, Wan, Fangbin, Wang, Jinjun, Wu, Yang, Huang, Dong
Most of Multiple Object Tracking (MOT) approaches compute individual target features for two subtasks: estimating target-wise motions and conducting pair-wise Re-Identification (Re-ID). Because of the indefinite number of targets among video frames,
Externí odkaz:
http://arxiv.org/abs/2001.11180
The main challenge of Multiple Object Tracking (MOT) is the efficiency in associating indefinite number of objects between video frames. Standard motion estimators used in tracking, e.g., Long Short Term Memory (LSTM), only deal with single object, w
Externí odkaz:
http://arxiv.org/abs/1905.02292
Human doing actions will result in WiFi distortion, which is widely explored for action recognition, such as the elderly fallen detection, hand sign language recognition, and keystroke estimation. As our best survey, past work recognizes human action
Externí odkaz:
http://arxiv.org/abs/1904.11953
Deep convolutional neural network significantly boosted the capability of salient object detection in handling large variations of scenes and object appearances. However, convolution operations seek to generate strong responses on individual pixels,
Externí odkaz:
http://arxiv.org/abs/1904.00048
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