1 code implementation • 6 Mar 2023 • Xinhui Li, Mingjia Li, Yaxing Wang, Chuan-Xian Ren, Xiaojie Guo
Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features.
no code implementations • CVPR 2023 • You-Wei Luo, Chuan-Xian Ren
A novel masked OT (MOT) methodology on conditional distributions is proposed by defining a mask operation with label information.
no code implementations • 14 Mar 2022 • Yong-Hui Liu, Chuan-Xian Ren, Xiao-Lin Xu, Ke-Kun Huang
Specifically, we propose a novel alignment loss term that minimizes the kernel Bures-Wasserstein distance between the joint distributions.
no code implementations • 26 Feb 2022 • You-Wei Luo, Chuan-Xian Ren
As a fundamental problem in machine learning, dataset shift induces a paradigm to learn and transfer knowledge under changing environment.
no code implementations • 19 Feb 2022 • Geng-Xin Xu, Chuan-Xian Ren
Also, to strengthen characterization on the capillaries and the edges of blood vessels, we define a residual pyramid architecture which decomposes the spatial information in the decoding phase.
no code implementations • 8 Sep 2021 • Geng-Xin Xu, Chen Liu, Jun Liu, Zhongxiang Ding, Feng Shi, Man Guo, Wei Zhao, Xiaoming Li, Ying WEI, Yaozong Gao, Chuan-Xian Ren, Dinggang Shen
Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i. e., class centers) in a hyper-sphere manifold.
no code implementations • CVPR 2021 • You-Wei Luo, Chuan-Xian Ren
A conditional distribution matching network is proposed to learn the conditional invariant and discriminative features for UDA.
no code implementations • 25 Aug 2020 • Chuan-Xian Ren, PengFei Ge, Peiyi Yang, Shuicheng Yan
Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label information of the target domain is agnostic.
no code implementations • 24 Aug 2020 • Chuan-Xian Ren, PengFei Ge, Dao-Qing Dai, Hong Yan
KLN can simultaneously learn a more expressive kernel and label prediction distribution, thus, it can be used to improve the classification performance in both supervised and semi-supervised learning scenarios.
no code implementations • 23 Aug 2020 • Pengfei Ge, Chuan-Xian Ren, Jiashi Feng, Shuicheng Yan
By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders.
no code implementations • 23 Aug 2020 • You-Wei Luo, Chuan-Xian Ren, Dao-Qing Dai, Hong Yan
Second, batch-wise training of deep learning limits the characterization of the global structure.
1 code implementation • 23 Aug 2020 • Chuan-Xian Ren, You-Wei Luo, Xiao-Lin Xu, Dao-Qing Dai, Hong Yan
Consequently, the crucial point of image set recognition is to mine the intrinsic connection or structural information from the image batches with variations.
no code implementations • 14 Jun 2020 • Pengfei Ge, Chuan-Xian Ren, Dao-Qing Dai, Hong Yan
In this paper, we consider a more general application scenario where the label distributions of the source and target domains are not the same.
no code implementations • 20 Feb 2020 • You-Wei Luo, Chuan-Xian Ren, PengFei Ge, Ke-Kun Huang, Yu-Feng Yu
Second, the batch-wise training manner in deep learning limits the description of the global structure.
no code implementations • 14 May 2019 • Chuan-Xian Ren, Bo-Hua Liang, Zhen Lei
We derive a camera style adaptation framework to learn the style-based mappings between different camera views, from the target domain to the source domain, and then we can transfer the identity-based distribution from the source domain to the target domain on the camera level.
Domain Adaptive Person Re-Identification Person Re-Identification +1