1 code implementation • 16 May 2020 • Aibek Musaev, Jiangping Wang, Liang Zhu, Cheng Li, Yi Chen, Jialin Liu, Wanqi Zhang, Juan Mei, De Wang
In addition, we describe an illustrative example of the use of this dataset for tracking participants based on a head tracking model in an effort to minimize errors due to occlusion.
no code implementations • 28 Jul 2012 • Yiyuan She, Huanghuang Li, Jiangping Wang, Dapeng Wu
Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation.
no code implementations • 4 Aug 2018 • Elena Balashova, Vivek Singh, Jiangping Wang, Brian Teixeira, Terrence Chen, Thomas Funkhouser
We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function.
no code implementations • ICCV 2015 • Jiangping Wang, Kai Ma, Vivek Kumar Singh, Thomas Huang, Terrence Chen
3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology.
no code implementations • 29 Mar 2019 • Elena Balashova, Jiangping Wang, Vivek Singh, Bogdan Georgescu, Brian Teixeira, Ankur Kapoor
Automatic delineation and measurement of main organs such as liver is one of the critical steps for assessment of hepatic diseases, planning and postoperative or treatment follow-up.
no code implementations • CCL 2021 • Ning Yu, Jiangping Wang, Yu Shi, Jianyi Liu
“本文利用知网(HowNet)中的知识, 并将Word2vec模型的结构和思想迁移至义原表示学习过程中, 提出了一个基于义原表示学习的词向量表示方法。首先, 本文利用OpenHowNet获取义原知识库中的所有义原、所有中文词汇以及所有中文词汇和其对应的义原集合, 作为实验的数据集。然后, 基于Skip-gram模型, 训练义原表示学习模型, 进而获得词向量。最后, 通过词相似度任务、词义消歧任务、词汇类比和观察最近邻义原, 来评价本文提出的方法获取的词向量的效果。通过和基线模型比较, 发现本文提出的方法既高效又准确, 不依赖大规模语料也不需要复杂的网络结构和繁多的参数, 也能提升各种自然语言处理任务的准确率。”