no code implementations • 26 Aug 2022 • Lingsheng Kong, Bo Hu, Xiongchang Liu, Jun Lu, Jane You, Xiaofeng Liu
Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain.
no code implementations • 1 Jan 2021 • Xiaofeng Liu, Linghao Jin, Xu Han, Jun Lu, Jane You, Lingsheng Kong
In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network.
no code implementations • 1 Jan 2021 • Xiaofeng Liu, Bo Hu, Xiongchang Liu, Jun Lu, Jane You, Lingsheng Kong
Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain.
no code implementations • ICCV 2019 • Xiaofeng Liu, Zhenhua Guo, Site Li, Lingsheng Kong, Ping Jia, Jane You, B. V. K. Kumar
We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images.
1 code implementation • Nature Communicationsvolume 10, Article number: 3474 (2019) 2019 • Yimin Wang, Qi Li, Li-Juan Liu, Zhi Zhou, Zongcai Ruan, Lingsheng Kong, Yaoyao Li, Yun Wang, Ning Zhong, Renjie Chai, Xiangfeng Luo, Yike Guo, Michael Hawrylycz, Qingming Luo, Zhongze Gu, Wei Xie, Hongkui Zeng, Hanchuan Peng
Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection.