Novel Motion Patterns Matter for Practical Skeleton-Based Action Recognition
Authors: Mengyuan Liu, Fanyang Meng, Chen Chen, Songtao Wu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on our newly collected dataset verify that Mask-GCN outperforms most GCN-based methods when testing with various novel motion patterns. |
| Researcher Affiliation | Collaboration | Mengyuan Liu1*, Fanyang Meng2, Chen Chen3, Songtao Wu4 1 Key Laboratory of Machine Perception, Peking University, Shenzhen Graduate School 2 Peng Cheng Laboratory 3 University of Central Florida 4 Sony R&D Center China |
| Pseudocode | No | The paper describes methods with formulas and block diagrams (Fig. 2, 3), but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code release. |
| Open Datasets | No | To evaluate our method, we use the pipeline shown in Fig. 4 to collect a new dataset. There are 21780 3D skeleton sequences in our dataset. |
| Dataset Splits | Yes | Four types of evaluation protocols are performed, i.e., cross-subject recognition with low training data (CS 1), cross-subject recognition with more training data (CS 2), cross-view recognition with low training data (CV 1), cross-view recognition with more training data (CV 2). Specifically, CS 1 uses 10 subjects for training, CS 2 uses 20 subjects for training, CV 1 uses 1 view for training, and CV 2 uses 2 views for training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not specify the version numbers of software dependencies used in the experiments. |
| Experiment Setup | Yes | We set τ to 0.01 as the default for our policy network. |