Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Yu, Ruichi"'
Autor:
Jing, Longlong, Yu, Ruichi, Chen, Xu, Zhao, Zhengli, Sheng, Shiwei, Graber, Colin, Chen, Qi, Li, Qinru, Wu, Shangxuan, Deng, Han, Lee, Sangjin, Sweeney, Chris, He, Qiurui, Hung, Wei-Chih, He, Tong, Zhou, Xingyi, Moussavi, Farshid, Guo, Zijian, Zhou, Yin, Tan, Mingxing, Yang, Weilong, Li, Congcong
Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in
Externí odkaz:
http://arxiv.org/abs/2405.00236
Autor:
Li, Yingwei, Chen, Tiffany, Kabkab, Maya, Yu, Ruichi, Jing, Longlong, You, Yurong, Zhao, Hang
Estimating the distance of objects is a safety-critical task for autonomous driving. Focusing on short-range objects, existing methods and datasets neglect the equally important long-range objects. In this paper, we introduce a challenging and under-
Externí odkaz:
http://arxiv.org/abs/2206.04831
Autor:
Jing, Longlong, Yu, Ruichi, Kretzschmar, Henrik, Li, Kang, Qi, Charles R., Zhao, Hang, Ayvaci, Alper, Chen, Xu, Cower, Dillon, Li, Yingwei, You, Yurong, Deng, Han, Li, Congcong, Anguelov, Dragomir
Publikováno v:
ICRA2022
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving. Approaches to monocular 3D perception including detection and tracking, however, often yield inferior performance
Externí odkaz:
http://arxiv.org/abs/2206.03666
Autor:
Hung, Wei-Chih, Kretzschmar, Henrik, Lin, Tsung-Yi, Chai, Yuning, Yu, Ruichi, Yang, Ming-Hsuan, Anguelov, Dragomir
Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars. Tracking objects, however, remains a highly challenging problem, especially in cluttered autonomous driving scenes in which objects tend to interact with
Externí odkaz:
http://arxiv.org/abs/2008.07725
Unconstrained video-based face recognition is a challenging problem due to significant within-video variations caused by pose, occlusion and blur. To tackle this problem, an effective idea is to propagate the identity from high-quality faces to low-q
Externí odkaz:
http://arxiv.org/abs/1905.02756
Recent advances in deep convolutional neural networks (CNNs) have motivated researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local structure has been proven to be important for the success of convolutional architectures
Externí odkaz:
http://arxiv.org/abs/1811.07782
We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance. We introduce a representation of the geometry and topology of scene layo
Externí odkaz:
http://arxiv.org/abs/1804.01429
This paper addresses the problem of detecting relevant motion caused by objects of interest (e.g., person and vehicles) in large scale home surveillance videos. The traditional method usually consists of two separate steps, i.e., detecting moving obj
Externí odkaz:
http://arxiv.org/abs/1801.02031
We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy. Conditioned upon a new c
Externí odkaz:
http://arxiv.org/abs/1711.08447
Autor:
Yu, Ruichi, Li, Ang, Chen, Chun-Fu, Lai, Jui-Hsin, Morariu, Vlad I., Han, Xintong, Gao, Mingfei, Lin, Ching-Yung, Davis, Larry S.
To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering statistics of an individual layer or two consecutive layers (e.g., prune one layer to minimize the reconstructi
Externí odkaz:
http://arxiv.org/abs/1711.05908