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..
Learning Graph-based Residual Aggregation Network for Group Activity Recognition
Authors: Wei Li, Tianzhao Yang, Xiao Wu, Zhaoquan Yuan
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on two popular benchmarks for group activity recognition clearly demonstrate the superior performance of our method in comparison with the state-of-the-art methods. |
| Researcher Affiliation | Academia | Wei Li , Tianzhao Yang , Xiao Wu and Zhaoquan Yuan School of Computing and Artificial Intelligence, Southwest Jiaotong University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block is provided in the paper. |
| Open Source Code | No | No statement or link is provided indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Two popular benchmarks (Volleyball Dataset (VD) [Ibrahim et al., 2016] and Collective Activity Dataset (CAD) [Choi et al., 2009]) are used to evaluate our proposed method |
| Dataset Splits | No | The paper does not explicitly provide specific percentages or sample counts for training, validation, and test splits, nor does it reference predefined splits with explicit details. It states: "randomly sampling frames from a video clip are selected as the training samples on both two datasets". |
| Hardware Specification | No | No specific hardware details (e.g., GPU or CPU models, memory specifications) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions "Our model is implemented based on Pytorch" but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The ADAM optimizer with different learning rates is used to learn the network parameters. The hyper-parameters for ADAM are set as β1 = 0.9, β2 = 0.999 and ϵ = 10 8. For the training of 40 epochs on VD, the initial learning rate is set to 1 10 4 with the dacay rate is 1/3 every 10 epochs. For the training of 30 epochs on CAD, the learning rates are set to 4 10 5 and 1 10 4 for Res Net18 and VGG16, respectively. The spatial constraint factors δ are set to 0.2, 0.3 of the image width in the training of VD and CAD, empirically. L is set to 16. The batch sizes are set to 2 on both datasets. |