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pro vyhledávání: '"Yuan, Bodi"'
The success of data mixing augmentations in image classification tasks has been well-received. However, these techniques cannot be readily applied to object detection due to challenges such as spatial misalignment, foreground/background distinction,
Externí odkaz:
http://arxiv.org/abs/2303.10343
Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces
In recent years, impressive results have been achieved in robotic manipulation. While many efforts focus on generating collision-free reference signals, few allow safe contact between the robot bodies and the environment. However, in human's daily ma
Externí odkaz:
http://arxiv.org/abs/2211.08199
We present a novel semi-supervised semantic segmentation method which jointly achieves two desiderata of segmentation model regularities: the label-space consistency property between image augmentations and the feature-space contrastive property amon
Externí odkaz:
http://arxiv.org/abs/2108.09025
Many applications of unpaired image-to-image translation require the input contents to be preserved semantically during translations. Unaware of the inherently unmatched semantics distributions between source and target domains, existing distribution
Externí odkaz:
http://arxiv.org/abs/2012.04932
Publikováno v:
In Engineering Applications of Artificial Intelligence November 2023 126 Part B
Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in sub-optimal solutions and is ex
Externí odkaz:
http://arxiv.org/abs/1904.09503
The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we onl
Externí odkaz:
http://arxiv.org/abs/1903.00640
Akademický článek
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The success of data mixing augmentations in image classification tasks has been well-received. However, these techniques cannot be readily applied to object detection due to challenges such as spatial misalignment, foreground/background distinction,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d7fb337d892885316f2df3404c021bd8
http://arxiv.org/abs/2303.10343
http://arxiv.org/abs/2303.10343