Search Results for author: Ding Li

Found 8 papers, 2 papers with code

Multi-scale 2D Representation Learning for weakly-supervised moment retrieval

no code implementations4 Nov 2021 Ding Li, Rui Wu, Yongqiang Tang, Zhizhong Zhang, Wensheng Zhang

Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates.

Moment Retrieval Representation Learning

DistFL: Distribution-aware Federated Learning for Mobile Scenarios

1 code implementation22 Oct 2021 Bingyan Liu, Yifeng Cai, Ziqi Zhang, Yuanchun Li, Leye Wang, Ding Li, Yao Guo, Xiangqun Chen

Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.

Federated Learning

Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation

no code implementations13 Dec 2020 Kun Zhang, Rui Wu, Ping Yao, Kai Deng, Ding Li, Renbiao Liu, Chuanguang Yang, Ge Chen, Min Du, Tianyao Zheng

We note that 2D pose estimation task is highly dependent on the contextual relationship between image patches, thus we introduce a self-supervised method for pretraining 2D pose estimation networks.

Pose Estimation

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

no code implementations15 May 2020 Lei Cai, Zhengzhang Chen, Chen Luo, Jiaping Gui, Jingchao Ni, Ding Li, Haifeng Chen

Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.

Anomaly Detection Network Embedding

SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection

1 code implementation25 Jun 2018 Peng Gao, Xusheng Xiao, Ding Li, Zhichun Li, Kangkook Jee, Zhen-Yu Wu, Chung Hwan Kim, Sanjeev R. Kulkarni, Prateek Mittal

To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies.

Cryptography and Security Databases

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