Unsupervised Action Segmentation via Fast Learning of Semantically Consistent Actoms
Authors: Zheng Xing, Weibing Zhao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 {zhengxing, weibingzhao}@link.cuhk.edu.cn |
| 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. |