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..
Novel Motion Patterns Matter for Practical Skeleton-Based Action Recognition
Authors: Mengyuan Liu, Fanyang Meng, Chen Chen, Songtao Wu
AAAI 2023 | Venue PDF | 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. |