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..
Recovering Complete Actions for Cross-dataset Skeleton Action Recognition
Authors: Hanchao Liu, Yujiang Li, Tai-Jiang Mu, Shi-min Hu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach on a cross-dataset setting with three skeleton action datasets, outperforming other domain generalization approaches by a considerable margin. We improve the average accuracy on unseen datasets by 5%, outperforming other baseline methods by a large margin. |
| Researcher Affiliation | Academia | Hanchao Liu1 Yujiang Li1 Tai-Jiang Mu1 Shi-Min Hu1 1BNRist, Department of Computer Science and Technology, Tsinghua University |
| Pseudocode | Yes | A1. Algorithm The algorithmic description of our recover-and-resample augmentation framework is provided in Algorithm 1. |
| Open Source Code | Yes | Code is available at https: //github.com/Hanchao Liu/Recover-and-Resample |
| Open Datasets | Yes | We use four large-scale datasets, i.e, NTU60-RGBD [41], PKU-MMD [25], ETRI-Activity3D [15] and Kinetics [5]. For term of use, NTU60-RGBD [41] is free for research and non-commercial use. We submitted a license agreement to ETRI-Activity3D [15] website for downloading the dataset. License for PKU-MMD [25] is not stated on its official homepage. |
| Dataset Splits | No | For skeleton action recognition, most works [42, 6] report best results on the test set since there is no official validation set. We also follow this evaluation protocol for our domain generalization task. |
| Hardware Specification | Yes | We implement our method and other baselines using Py Torch [36] and conduct experiments on a single NVIDIA RTX 2080Ti. |
| Software Dependencies | No | The paper mentions 'Py Torch [36]' but does not specify a version number. |
| Experiment Setup | Yes | We set training hyper-parameters the same as [42]. Furthermore, we set λT = 0.1, the number of linear transforms Ntr = 20, the number of background poses Nbkg = 10, and maug = 0.75. We set α = 0.1 for sampling tp. We fix the resampling method by randomly sampling a segment with length ratio r between 0.7 and 1.0. We use k-means for clustering boundary poses and linear transforms. |