1 code implementation • 27 Jan 2024 • Haocong Rao, Chunyan Miao
Person re-identification via 3D skeletons is an important emerging research area that triggers great interest in the pattern recognition community.
1 code implementation • 24 Jul 2023 • Haocong Rao, Cyril Leung, Chunyan Miao
Then a hierarchical meta-prototype contrastive learning model is proposed to cluster and contrast the most typical skeleton features ("prototypes") from different-level skeletons.
2 code implementations • CVPR 2023 • Haocong Rao, Chunyan Miao
Then, we propose the Graph Prototype Contrastive learning (GPC) to mine the most typical graph features (graph prototypes) of each identity, and contrast the inherent similarity between graph representations and different prototypes from both skeleton and sequence levels to learn discriminative graph representations.
1 code implementation • 1 Mar 2023 • Haocong Rao, Cyril Leung, Chunyan Miao
We further propose three evaluation metrics to measure the consistency, robustness, and fairness of assessment results from state-of-the-art LLMs including ChatGPT and GPT-4.
1 code implementation • 25 Aug 2022 • Haocong Rao, Chunyan Miao
Lastly, we propose a skeleton prototype contrastive learning scheme that clusters feature-correlative instances of unlabeled graph representations and contrasts their inherent similarity with representative skeleton features ("skeleton prototypes") to learn discriminative skeleton representations for person re-ID.
1 code implementation • 21 Apr 2022 • Haocong Rao, Chunyan Miao
Specifically, to fully exploit skeleton features within each skeleton sequence, we first devise a masked prototype contrastive learning (MPC) scheme to cluster the most typical skeleton features (skeleton prototypes) from different subsequences randomly masked from raw sequences, and contrast the inherent similarity between skeleton features and different prototypes to learn discriminative skeleton representations without using any label.
1 code implementation • 5 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.
1 code implementation • 6 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.
1 code implementation • 14 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.
1 code implementation • 5 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.
1 code implementation • 21 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.
2 code implementations • 1 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.