Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Graph Contrastive Learning for Skeleton-based Action Recognition
Authors: Xiaohu Huang, Hao Zhou, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang, Xinggang Wang, Wenyu Liu, Bin Feng
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We combine Skeleton GCL with three GCNs (2S-ACGN, CTR-GCN, and Info GCN), and achieve consistent improvements on NTU60, NTU120, and NW-UCLA benchmarks.4 EXPERIMENTS |
| Researcher Affiliation | Collaboration | Xiaohu Huang 1, 2 Hao Zhou 2 Jian Wang 2 Haocheng Feng 2 Junyu Han 2 Errui Ding 2 Jingdong Wang 2 Xinggang Wang 1 Wenyu Liu 1 Bin Feng 1 1 School of EIC, Huazhong University of Science & Technology 2 Department of Computer Vision Technology (VIS), Baidu Inc., China |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The source code will be available at https://github.com/Oliver Hxh/Skeleton GCL. |
| Open Datasets | Yes | NTU RGB+D (NTU60) (Shahroudy et al., 2016) is a large-scale skeleton-based action recognition dataset... NTU RGB+D 120 (NTU120) (Liu et al., 2019) is an extension of NTU RGB+D dataset... Northwestern-UCLA (NW-UCLA) dataset (Wang et al., 2014) contains 1494 sequences from 10 action classes. |
| Dataset Splits | Yes | Generally, two protocols are used to evaluate the performances: (1) cross-subject (X-Sub): train data are performed by 20 subjects, and test data are performed by other 20 subjects. (2) cross-view (X-View): train data from cameras 2 and 3, and test data from camera 1. |
| Hardware Specification | Yes | All experiments are conducted using a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | P, the number of stored instances for each class in MIns, is set as 684 on NTU60 and NTU120, and 342 on NW-UCLA. The dimension of graph vector Cg is set to 256. For all datasets, the number of sampling examples K+ H , K H , and K R are set as 128, 512, and 512, respectively. For different models used in different modalities, we experiment with temperature τ of 0.5, 0.8, 1.0, and 1.5, and choose the best one. The hyper-parameter α for momentum updating is set as 0.85. |