no code implementations • 23 Oct 2023 • Yongsong Huang, Wanqing Xie, Mingzhen Li, Mingmei Cheng, Jinzhou Wu, Weixiao Wang, Jane You, Xiaofeng Liu
However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i. e., non-i. i. d.)
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 • ICCV 2021 • Xiaofeng Liu, Site Li, Yubin Ge, Pengyi Ye, Jane You, Jun Lu
The UDA for ordinal classification requires inducing non-trivial ordinal distribution prior to the latent space.
no code implementations • ICCV 2021 • Xiaofeng Liu, Zhenhua Guo, Site Li, Fangxu Xing, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w. r. t.
no code implementations • 30 Apr 2021 • Yubin Ge, Site Li, Xuyang Li, Fangfang Fan, Wanqing Xie, Jane You, Xiaofeng Liu
The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk.
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 • 1 Jan 2021 • Xiaofeng Liu, Xiongchang Liu, Bo Hu, Wenxuan Ji, Fangxu Xing, Jun Lu, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids.
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 • 21 Oct 2020 • Xiaofeng Liu, Yuzhuo Han, Song Bai, Yi Ge, Tianxing Wang, Xu Han, Site Li, Jane You, Ju Lu
However, the cross entropy loss can not take the different importance of each class in an self-driving system into account.
no code implementations • 20 Oct 2020 • Xiaofeng Liu, Linghao Jin, Xu Han, Jane You
In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possibly extract identity factors from the I frame with a pre-trained face recognition network.
no code implementations • 11 Aug 2020 • Xiaofeng Liu, Yimeng Zhang, Xiongchang Liu, Song Bai, Site Li, Jane You
The ground metric of Wasserstein distance can be pre-defined following the experience on a specific task.
no code implementations • ECCV 2020 • Xiaofeng Liu, Tong Che, Yiqun Lu, Chao Yang, Site Li, Jane You
This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision.
no code implementations • ICCV 2019 • Xiaofeng Liu, Yang Zou, Tong Che, Peng Ding, Ping Jia, Jane You, Kumar B. V. K
We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i. e.,$ using arc length of a circle) or adaptively learning the ground metric.
no code implementations • 5 Aug 2019 • Xiaofeng Liu, Zhenhua Guo, Jane You, B. V. K. Vijaya Kumar
The importance of each image is usually considered either equal or based on a quality assessment of that image independent of other images and/or videos in that image set.
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.
no code implementations • ECCV 2018 • Xiaofeng Liu, B. V. K. Vijaya Kumar, Chao Yang, Qingming Tang, Jane You
This paper targets the problem of image set-based face verification and identification.
2 code implementations • 24 Jun 2019 • Mu Li, Kede Ma, Jane You, David Zhang, WangMeng Zuo
For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation.
no code implementations • 17 Apr 2019 • Qiang Li, Bo Xie, Jane You, Wei Bian, DaCheng Tao
In this paper, we present correlated logistic (CorrLog) model for multilabel image classification.
1 code implementation • 1 Apr 2019 • Mu Li, WangMeng Zuo, Shuhang Gu, Jane You, David Zhang
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance.