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..
Temporal Segmentation of Fine-gained Semantic Action: A Motion-Centered Figure Skating Dataset
Authors: Shenglan Liu, Aibin Zhang, Yunheng Li, Jian Zhou, Li Xu, Zhuben Dong, Renhao Zhang2163-2171
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that existing state-of-the-art methods are difficult to achieve excellent segmentation results (including accuracy, edit and F1 score) in the MCFS dataset. |
| Researcher Affiliation | Collaboration | 1 Dalian University of Technology, Dalian, Liaoning, 116024 China 2 Alibaba Group |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link for the dataset ('The latest dataset can be downloaded at https://shenglanliu.github.io/mcfs-dataset/.') but does not provide a link or explicit statement about the availability of the source code for the methodology described. |
| Open Datasets | Yes | In order to explore more models and practical applications of motion-centered TAS, we introduce a Motion-Centered Figure Skating (MCFS) dataset in this paper. The latest dataset can be downloaded at https://shenglanliu.github.io/mcfs-dataset/. |
| Dataset Splits | Yes | MCFS is randomly split into 189 and 82 videos for training and testing, respectively. Then, we utilize 5-fold cross validation to assess generalization of the models. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions software like 'Open Pose' and models like 'I3D' but does not provide specific version numbers for any key software components or libraries required for reproducibility. |
| Experiment Setup | No | The paper mentions data splits and feature extraction details but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs) or optimizer settings. |