1 code implementation • 5 Jan 2024 • Yabin Zhu, Xiao Wang, Chenglong Li, Bo Jiang, Lin Zhu, Zhixiang Huang, Yonghong Tian, Jin Tang
In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras.
1 code implementation • 16 Dec 2023 • Yijun Li, Cheuk Hang Leung, Xiangqian Sun, Chaoqun Wang, Yiyan Huang, Xing Yan, Qi Wu, Dongdong Wang, Zhixiang Huang
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales.
1 code implementation • 5 Dec 2023 • Junjie Gao, Xiangyu Zheng, Dongdong Wang, Zhixiang Huang, Bangqi Zheng, Kai Yang
Uplift modeling refers to the set of machine learning techniques that a manager may use to estimate customer uplift, that is, the net effect of an action on some customer outcome.
no code implementations • 26 Aug 2023 • Chaoqun Wang, Yijun Li, Xiangqian Sun, Qi Wu, Dongdong Wang, Zhixiang Huang
The tensorized LSTM assigns each variable with a unique hidden state making up a matrix $\mathbf{h}_t$, and the standard LSTM models all the variables with a shared hidden state $\mathbf{H}_t$.
1 code implementation • 31 May 2023 • Shumin Ma, Zhiri Yuan, Qi Wu, Yiyan Huang, Xixu Hu, Cheuk Hang Leung, Dongdong Wang, Zhixiang Huang
This paper proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals.
no code implementations • 26 Mar 2023 • Yabin Zhu, Chenglong Li, Xiao Wang, Jin Tang, Zhixiang Huang
In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy.
no code implementations • 5 Sep 2022 • Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu, Shumin Ma, Zhiri Yuan, Dongdong Wang, Zhixiang Huang
Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue.
no code implementations • 5 Sep 2022 • Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Qi Wu, Dongdong Wang, Zhixiang Huang
In this paper, we propose a moderately-balanced representation learning (MBRL) framework based on recent covariates balanced representation learning methods and orthogonal machine learning theory.
1 code implementation • 11 Feb 2022 • Yabin Zhu, Chenglong Li, Yao Liu, Xiao Wang, Jin Tang, Bin Luo, Zhixiang Huang
Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection and segmentation.
no code implementations • 17 Dec 2020 • Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu, Nanbo Peng, Dongdong Wang, Zhixiang Huang
Classical estimators overlook the confounding effects and hence the estimation error can be magnificent.