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 [1].
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. |