no code implementations • Findings (NAACL) 2022 • Jiaheng Liu, Tan Yu, Hanyu Peng, Mingming Sun, Ping Li
Existing multilingual video corpus moment retrieval (mVCMR) methods are mainly based on a two-stream structure.
no code implementations • 11 Jan 2023 • Zixiang Wang, Jian Yang, Tongliang Li, Jiaheng Liu, Ying Mo, Jiaqi Bai, Longtao He, Zhoujun Li
In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages.
1 code implementation • 6 Dec 2022 • Honghui Yang, Tong He, Jiaheng Liu, Hua Chen, Boxi Wu, Binbin Lin, Xiaofei He, Wanli Ouyang
In contrast to previous 3D MAE frameworks, which either design a complex decoder to infer masked information from maintained regions or adopt sophisticated masking strategies, we instead propose a much simpler paradigm.
no code implementations • 17 Nov 2022 • Jiaheng Liu, Tong He, Honghui Yang, Rui Su, Jiayi Tian, Junran Wu, Hongcheng Guo, Ke Xu, Wanli Ouyang
Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness.
no code implementations • 19 Oct 2022 • Hongcheng Guo, Jiaheng Liu, Haoyang Huang, Jian Yang, Zhoujun Li, Dongdong Zhang, Zheng Cui, Furu Wei
To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages.
no code implementations • 23 Aug 2022 • Hongcheng Guo, Yuhui Guo, Jian Yang, Jiaheng Liu, Zhoujun Li, Tieqiao Zheng, Weichao Hou, Liangfan Zheng, Bo Zhang
Experiments on five benchmarks validate the effectiveness of LogLG for detecting anomalies on unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly supervised method, achieves significant performance improvements compared to existing methods.
1 code implementation • 22 Aug 2022 • Chengwei Pan, Baolian Qi, Gangming Zhao, Jiaheng Liu, Chaowei Fang, Dingwen Zhang, Jinpeng Li
In CTN, a transformer module is constructed in parallel to a U-Net to learn long-distance dependencies between different anatomical regions; and these dependencies are communicated to the U-Net at multiple stages to endow it with global awareness.
1 code implementation • 1 Jul 2022 • Chengwei Pan, Gangming Zhao, Junjie Fang, Baolian Qi, Jiaheng Liu, Chaowei Fang, Dingwen Zhang, Jinpeng Li, Yizhou Yu
Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption.
no code implementations • 12 Apr 2022 • Jiaheng Liu, Haoyu Qin, Yichao Wu, Jinyang Guo, Ding Liang, Ke Xu
In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective face recognition distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework.
no code implementations • 2 Mar 2021 • Jiaheng Liu, Yudong Wu, Yichao Wu, Zhenmao Li, Chen Ken, Ding Liang, Junjie Yan
In this study, we make a key observation that the local con-text represented by the similarities between the instance and its inter-class neighbors1plays an important role forFR.
no code implementations • ICCV 2021 • Jiaheng Liu, Yudong Wu, Yichao Wu, Chuming Li, Xiaolin Hu, Ding Liang, Mengyu Wang
To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively.
1 code implementation • 9 Feb 2020 • Jiaheng Liu, Guo Lu, Zhihao Hu, Dong Xu
Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
no code implementations • 27 May 2019 • Zhenmao Li, Yichao Wu, Ken Chen, Yudong Wu, Shunfeng Zhou, Jiaheng Liu, Junjie Yan
Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters.
1 code implementation • ICCV 2019 • Xiao Jin, Baoyun Peng, Yi-Chao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Xiaolin Hu
However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss.
2 code implementations • ICCV 2019 • Baoyun Peng, Xiao Jin, Jiaheng Liu, Shunfeng Zhou, Yi-Chao Wu, Yu Liu, Dongsheng Li, Zhaoning Zhang
Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level.
no code implementations • 28 Feb 2019 • Yingcheng Su, Shunfeng Zhou, Yi-Chao Wu, Tian Su, Ding Liang, Jiaheng Liu, Dixin Zheng, Yingxu Wang, Junjie Yan, Xiaolin Hu
Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications.