Timestamp-Supervised Action Segmentation from the Perspective of Clustering
Authors: Dazhao Du, Enhan Li, Lingyu Si, Fanjiang Xu, Fuchun Sun
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the effectiveness of our method. |
| Researcher Affiliation | Academia | 1Institute of Software, Chinese Academy of Science 2University of Chinese Academy of Sciences 3Institute of Computing Technology, Chinese Academy of Sciences 4Department of Computer Science and Technology, Tsinghua University |
| Pseudocode | Yes | Algorithm 1 The iterative clustering algorithm |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | The 50Salads dataset [Stein and Mc Kenna, 2013] |
| Dataset Splits | Yes | We perform fourfold cross-validation on the GTEA and Breakfast datasets, and five-fold cross-validation on the 50Salads dataset. For evaluation, we report the average of all the splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as programming language versions or library versions (e.g., PyTorch, TensorFlow, scikit-learn). |
| Experiment Setup | Yes | We train the model for 70 epochs with Adam optimizer [Kingma and Ba, 2014]. Similar to [Li et al., 2021], in the first 50 epochs, we only use the annotated timestamps to train to find a good initialization. Subsequently, we apply the IC algorithm to train for 20 epochs. We use the learning rate 0.0001 for Breakfast and 0.0005 for GTEA and 50Salads. We set λ, β, γ to 0.15, 0.075 and 0.15 respectively. |