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..
Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition
Authors: Kun Li, Dan Guo, Guoliang Chen, Chunxiao Fan, Jingyuan Xu, Zhiliang Wu, Hehe Fan, Meng Wang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on the benchmark dataset demonstrate the superior performance of our method compared to existing approaches. |
| Researcher Affiliation | Academia | 1School of Computer Science and Information Engineering, Hefei University of Technology 2 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center 3Re LER Lab, CCAI, Zhejiang University |
| Pseudocode | No | The paper describes the methodology using text and diagrams (Figure 2) but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to the MA-52 dataset (https://github.com/VUT-HFUT/Micro-Action) but does not explicitly state that the source code for the proposed PCAN methodology is open-source or provide a link to its implementation. |
| Open Datasets | Yes | Extensive experiments conducted on the public microaction dataset, MA-52, validate the effectiveness of the proposed method. [...] 1https://github.com/VUT-HFUT/Micro-Action |
| Dataset Splits | Yes | The dataset consists of 11,250, 5,586, and 5,586 instances in training/validation/test, respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions using the SGD optimizer but does not specify software dependencies like programming languages, libraries, or frameworks with their version numbers. |
| Experiment Setup | Yes | For model training, we adopt the SGD optimizer with a learning rate of 0.0075, a momentum of 0.9, a weight decay of 1e-4, and a batch size of 10. The learning rate is reduced by a factor of 10 at the 15th and 30th epochs, and the model is trained with 40 epochs. In Eq. 10, we set γ = 1 for the RGB branch and γ = 5 for the Pose branch. In Eq. 12, we set β = 5 for the RGB branch and β = 5 for the Pose branch. |