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..
Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition
Authors: Jiahang Zhang, Lilang Lin, Jiaying Liu
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three large-scale skeleton datasets show our remarkable performance improvement under both long-tailed and balanced settings. |
| Researcher Affiliation | Academia | Jiahang Zhang , Lilang Lin , Jiaying Liu Wangxuan Institute of Computer Technology, Peking University EMAIL |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our project is publicly available at: https://jhang2020.github.io/Projects/Shap Mix/Shap-Mix.html. |
| Open Datasets | Yes | NTU RGB+D 60 Dataset (NTU 60) [Shahroudy et al., 2016]. NTU RGB+D 120 Dataset (NTU 120) [Liu et al., 2019]. Kinetics Skeleton 400 (Kinetics 400) [Kay et al., 2017]. |
| Dataset Splits | Yes | Two evaluation protocols are recommended: a) Cross-Subject (xsub): the training data are collected from 20 subjects, while the testing data are from the other 20 subjects. b) Cross-View (xview): the front and two side captured views are used for training, while testing set includes the left and right 45-degree views. Two recommended protocols are presented: a) Cross-Subject (xsub): the training data are collected from 53 subjects, while the other 53 subjects are for testing. b) Cross-Setup (xset): the training data use even setup IDs, while testing data use odd ones. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions "CTR-GCN" as a backbone and "Open Pose toolbox" for extracting data, but does not provide specific version numbers for these or any other software dependencies (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | For the implementation of Shap-Mix, we randomly sample 2 or 3 body parts to mix in spatial dimension. The mixed temporal length is from 40% to 70% of the original length. The temperature τ is set as 0.2, and the momentum coefficient in the EMA is 0.9. The warm-up phase is for the first 5 epochs. Due to the less data in the constructed long-tailed dataset, we increase the training epochs to 100. |