1 code implementation • ICCV 2023 • Dingkang Yang, Shuai Huang, Zhi Xu, Zhenpeng Li, Shunli Wang, Mingcheng Li, Yuzheng Wang, Yang Liu, Kun Yang, Zhaoyu Chen, Yan Wang, Jing Liu, Peixuan Zhang, Peng Zhai, Lihua Zhang
Driver distraction has become a significant cause of severe traffic accidents over the past decade.
no code implementations • 22 Feb 2021 • Donghui Yan, Jian Zou, Zhenpeng Li
Inspired by the recent advance in semi-supervised learning and deep learning, we propose mfTacoma to learn alternative deep representations in the context of TMA image scoring.
no code implementations • 3 Dec 2020 • Zhenpeng Li, Jianan Jiang, Yuhong Guo, Tiantian Tang, Chengxiang Zhuo, Jieping Ye
In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption.
1 code implementation • 8 Jun 2020 • Jianan Jiang, Zhenpeng Li, Yuhong Guo, Jieping Ye
The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.
no code implementations • 18 May 2020 • Bingyu Liu, Zhen Zhao, Zhenpeng Li, Jianan Jiang, Yuhong Guo, Jieping Ye
In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge.
no code implementations • 29 Mar 2020 • Zhenpeng Li, Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye
However, in practice the labeled data can come from multiple source domains with different distributions.
no code implementations • (IJCAI 2019 • Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, and Jun Gao
Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e. g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data.
no code implementations • 31 Dec 2018 • Donghui Yan, Yingjie Wang, Jin Wang, Honggang Wang, Zhenpeng Li
Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.