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..
Unsupervised Action Segmentation via Fast Learning of Semantically Consistent Actoms
Authors: Zheng Xing, Weibing Zhao
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach is evaluated on four benchmark datasets, and the results demonstrate that our method achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | 1 Future Network of Intelligence Institute, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China 2 Guangdong Laboratory of Machine Perception and Intelligent Computing, Shenzhen MSU-BIT University, China EMAIL |
| Pseudocode | Yes | Algorithm 1: The split-and-merge (Sa M) algorithm. |
| Open Source Code | Yes | Code is available at https://github.com/y66y/SaM. |
| Open Datasets | Yes | Datasets. We assessed the efficacy of our approach on four benchmark datasets: Breakfast (BF)(Kuehne, Arslan, and Serre 2014), You Tube Instructional Videos (YTI) (Alayrac et al. 2016), Hollywood Extended (HE) (Bojanowski et al. 2014), and 50Salads (FS) (Stein and Mc Kenna 2013). |
| Dataset Splits | No | The paper mentions using benchmark datasets and discusses "split 1 on BF" for runtime comparison but does not provide specific train/validation/test dataset splits (percentages, sample counts, or explicit methodology for splitting) for reproducibility. |
| Hardware Specification | No | The paper mentions 'hours of GPU-intensive model training' but does not provide specific hardware details such as GPU/CPU models, memory, or processor types used for experiments. |
| Software Dependencies | No | No specific ancillary software details, such as library names with version numbers, were provided to replicate the experiment. |
| Experiment Setup | Yes | In the upcoming experimental section, we will conduct a detailed analysis of how the window size δ affects the performance of our algorithm. Furthermore, we will illustrate the robustness of our algorithm in response to changes in δ. |