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..
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
Authors: Yuhang Wen, Mengyuan Liu, Songtao Wu, Beichen Ding
NeurIPS 2024 | Venue PDF | 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 EMAIL Mengyuan Liu State Key Laboratory of General Artificial Intelligence Peking University, Shenzhen Graduate School EMAIL Songtao Wu Sony R&D Center China EMAIL Beichen Ding Sun Yat-sen University EMAIL |
| 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. |