Search Results for author: Xiping Hu

Found 12 papers, 6 papers with code

SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification

1 code implementation5 Jul 2021 Haocong Rao, Xiping Hu, Jun Cheng, Bin Hu

In this paper, we for the first time propose a Self-supervised Multi-scale Skeleton Graph Encoding (SM-SGE) framework that comprehensively models human body, component relations, and skeleton dynamics from unlabeled skeleton graphs of various scales to learn an effective skeleton representation for person Re-ID.

Person Re-Identification

More than Encoder: Introducing Transformer Decoder to Upsample

no code implementations20 Jun 2021 Yijiang Li, Wentian Cai, Ying Gao, Xiping Hu

AU leverages pixel-level attention to model long range dependency and global information for better reconstruction.

Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification

1 code implementation6 Jun 2021 Haocong Rao, Shihao Xu, Xiping Hu, Jun Cheng, Bin Hu

To fully explore body relations, we construct graphs to model human skeletons from different levels, and for the first time propose a Multi-level Graph encoding approach with Structural-Collaborative Relation learning (MG-SCR) to encode discriminative graph features for person Re-ID.

Person Re-Identification

Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition

1 code implementation14 Nov 2020 Shihao Xu, Haocong Rao, Xiping Hu, Bin Hu

Existing approaches usually learn action representations by sequential prediction but they suffer from the inability to fully learn semantic information.

Action Recognition Representation Learning +4

A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification

1 code implementation5 Sep 2020 Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Yi Guo, Jun Cheng, Xinwang Liu, Bin Hu

This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID.

Contrastive Learning Person Re-Identification +2

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

1 code implementation21 Aug 2020 Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Huang Da, Jun Cheng, Bin Hu

Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner.

Person Re-Identification

Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition

2 code implementations1 Aug 2020 Haocong Rao, Shihao Xu, Xiping Hu, Jun Cheng, Bin Hu

In this paper, we for the first time propose a contrastive action learning paradigm named AS-CAL that can leverage different augmentations of unlabeled skeleton data to learn action representations in an unsupervised manner.

Action Recognition Contrastive Learning

Emotion Recognition From Gait Analyses: Current Research and Future Directions

no code implementations13 Mar 2020 Shihao Xu, Jing Fang, Xiping Hu, Edith Ngai, Wei Wang, Yi Guo, Victor C. M. Leung

This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns.

Emotion Recognition

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

no code implementations20 Feb 2020 Hanshu Cai, Yiwen Gao, Shuting Sun, Na Li, Fuze Tian, Han Xiao, Jianxiu Li, Zhengwu Yang, Xiaowei Li, Qinglin Zhao, Zhenyu Liu, Zhijun Yao, Minqiang Yang, Hong Peng, Jing Zhu, Xiaowei Zhang, Guoping Gao, Fang Zheng, Rui Li, Zhihua Guo, Rong Ma, Jing Yang, Lan Zhang, Xiping Hu, Yumin Li, Bin Hu

The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.

EEG

Towards Interpreting Deep Neural Networks via Understanding Layer Behaviors

no code implementations25 Sep 2019 JieZhang Cao, Jincheng Li, Xiping Hu, Peilin Zhao, Mingkui Tan

ii) the $W$-distance of a specific layer to the target distribution tends to decrease along training iterations.

Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks

no code implementations22 Apr 2018 Fusheng Hao, Jun Cheng, Lei Wang, Xinchao Wang, Jianzhong Cao, Xiping Hu, Dapeng Tao

Discriminative features are obtained by constraining the deep CNNs to map training samples to the corresponding anchors as close as possible.

Image Classification

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