Zobrazeno 241 - 250
of 625
pro vyhledávání: '"Wang, Chengjie"'
Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semantic anomaly detection which detects anomaly at the label level (semantic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e682ea26e72e1e37e6e398ba536c212
Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of a 3D object from its partial observation. In this paper, we propose a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28324ccb2343c6d82448b43af4eacac3
Autor:
Huang, Dihe, Chen, Ying, Ding, Yikang, Liao, Jinli, Liu, Jianlin, Wu, Kai, Nie, Qiang, Liu, Yong, Wang, Chengjie, Li, Zhiheng
Bird's eye view (BEV) is widely adopted by most of the current point cloud detectors due to the applicability of well-explored 2D detection techniques. However, existing methods obtain BEV features by simply collapsing voxel or point features along t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::316448c5788e350b75a363f0052ed225
Autor:
Peng, Jinlong, Luo, Zekun, Liu, Liang, Zhang, Boshen, Wang, Tao, Wang, Yabiao, Tai, Ying, Wang, Chengjie, Lin, Weiyao
Image harmonization aims to generate a more realistic appearance of foreground and background for a composite image. Existing methods perform the same harmonization process for the whole foreground. However, the implanted foreground always contains d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fc97d97efa16f9d313bc0182c7d4981
Most prior work represents the shapes of point clouds by coordinates. However, it is insufficient to describe the local geometry directly. In this paper, we present \textbf{RepSurf} (representative surfaces), a novel representation of point clouds to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3bf9ad6d373495861372e943dc6071b
Autor:
Lai, Jinxiang, Yang, Siqian, Liu, Wenlong, Zeng, Yi, Huang, Zhongyi, Wu, Wenlong, Liu, Jun, Gao, Bin-Bin, Wang, Chengjie
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples. Most feature embedding modules in recent FSL methods are specially desi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a83fcf0f319c6f8480b818ebb6f03b52
Autor:
Lin, Lijun, Dickhoefer, Uta, Müller, Katrin, Wang, Chengjie, Glindemann, Thomas, Hao, Jun, Wan, Hongwei, Schönbach, Philipp, Gierus, Martin, Taube, Friedhelm, Susenbeth, Andreas
Publikováno v:
In Livestock Science March 2012 144(1-2):140-147
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