CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
Authors: Yuhang Wen, Mengyuan Liu, Songtao Wu, Beichen Ding
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six datasets, including NTU Mutual 11/26, H2O, Assembly101, Collective Activity and Volleyball, consistently verify our approach by seamlessly adapting to single-entity backbones and boosting their performance. |
| Researcher Affiliation | Collaboration | Yuhang Wen Sun Yat-sen University wenyh29@mail2.sysu.edu.cn Mengyuan Liu State Key Laboratory of General Artificial Intelligence Peking University, Shenzhen Graduate School nkliuyifang@gmail.com Songtao Wu Sony R&D Center China Songtao.Wu@sony.com Beichen Ding Sun Yat-sen University dingbch@mail.sysu.edu.cn |
| Pseudocode | Yes | Algorithm 1 CHASE Wrapper: Py Torch-like Pseudo Code |
| Open Source Code | Yes | Our code is publicly available at https://github.com/Necolizer/CHASE. |
| Open Datasets | Yes | We conduct experiments on six multi-entity action recognition datasets. ...NTU Mutual 11 and NTU Mutual 26, respectively subsets of NTU RGB+D [41] and NTU RGB+D 120 [42]... H2O [13]... Assembly101 (ASB101) [12]... Collective Activity Dataset (CAD) [85]... Volleyball Dataset (VD) [86]. |
| Dataset Splits | Yes | NTU Mutual 11 adopts the widely-used X-Sub and X-View criteria, while NTU Mutual 26 follows the X-Sub and X-Set criteria. ...We follow the training, validation, and test splits outlined in [13] in our experiments [for H2O]. ...We follow the training, validation, and test splits described in [12] for evaluations [for Assembly101]. |
| Hardware Specification | Yes | Experiments are conducted on the Ge Force RTX 3070 GPUs with Py Torch. ...Experiments are conducted with 8 Ge Force RTX 3070 GPUs (GPU Memory: 8GB)... |
| Software Dependencies | Yes | using torch version 1.9.0+cu111, torchvision version 0.10.0+cu111, and CUDA version 11.4. |
| Experiment Setup | Yes | For CTR-GCN in NTU Mutual 26, we adopt input shape X R3 64 25 2, segment size (1, 1, 1) and λ = 0.1 in CHASE. SGD optimizer is used with Nesterov momentum of 0.9, a initial learning rate of 0.1 and a decay rate 0.1 at the 80th and 100th epoch. Batch size is set to 64. More detailed configurations for each model are provided in the Appendix. |