Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
3D Single-Person Concurrent Activity Detection Using Stacked Relation Network
Authors: Yi Wei, Wenbo Li, Yanbo Fan, Linghan Xu, Ming-Ching Chang, Siwei Lyu12329-12337
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,556 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,991 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the effectiveness of SRN, we conduct experiments on the UCLA concurrent activity dataset (Wei et al. 2013), which provides 3D human skeleton sequences with concurrent activity annotations. |
| Researcher Affiliation | Collaboration | 1University at Albany, State University of New York, USA 2Samsung Research America AI Center, USA 3Tencent AI Laboratory, China, 4Tianjin University, China |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The new dataset and code are available at https://github.com/weiyi1991/UA Concurrent/ |
| Open Datasets | Yes | To evaluate the effectiveness of SRN, we conduct experiments on the UCLA concurrent activity dataset (Wei et al. 2013)... and The new dataset and code are available at https://github.com/weiyi1991/UA Concurrent/ |
| Dataset Splits | Yes | We follow the same experimental setting as in (Wei et al. 2013) to use sequences with even indices for training and the remaining for test. We use two thirds of the sequences for training and the remaining for test. |
| Hardware Specification | Yes | All experiments are conducted on a machine with an NVIDIA GTX1080Ti GPU with 11GB onboard memory. |
| Software Dependencies | No | Our method is implemented with Py Torch. This statement mentions the software but lacks a specific version number, which is required for reproducibility. |
| Experiment Setup | Yes | minimized using back-propagation with ADAM optimizer and learning rate 0.001, betas (0.9, 0.999). We use Nr = 4 parallel relation modules within an Srelation layer. Dimension of the relation feature df is 64, and dk in (2) is set as 8. We set the hidden dimension for each LSTM binary classi๏ฌer as 128. |