Search Results for author: Jiaheng Liu

Found 16 papers, 6 papers with code

Multilingual Entity and Relation Extraction from Unified to Language-specific Training

no code implementations11 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.

Relation Extraction

GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds

1 code implementation6 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.

3D-QueryIS: A Query-based Framework for 3D Instance Segmentation

no code implementations17 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.

3D Instance Segmentation Semantic Segmentation

LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation

no code implementations19 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.

Multimodal Machine Translation Translation

LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction

no code implementations23 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.

Anomaly Detection graph construction +1

Deep 3D Vessel Segmentation based on Cross Transformer Network

1 code implementation22 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.

Computed Tomography (CT)

Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance

1 code implementation1 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.

Relational Reasoning Weakly-supervised Learning

CoupleFace: Relation Matters for Face Recognition Distillation

no code implementations12 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.

Face Recognition Knowledge Distillation

Inter-class Discrepancy Alignment for Face Recognition

no code implementations2 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.

Face Recognition

DAM: Discrepancy Alignment Metric for Face Recognition

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.

Face Recognition

A Unified End-to-End Framework for Efficient Deep Image Compression

1 code implementation9 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.

Image Compression Video Compression

Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework

no code implementations27 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.

Knowledge Distillation via Route Constrained Optimization

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.

Face Recognition Knowledge Distillation

Correlation Congruence for Knowledge Distillation

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.

Face Recognition Image Classification +3

Dynamic Multi-path Neural Network

no code implementations28 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.

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