Recovering Complete Actions for Cross-dataset Skeleton Action Recognition
Authors: Hanchao Liu, Yujiang Li, Tai-Jiang Mu, Shi-min Hu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |