Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
OnlineTAS: An Online Baseline for Temporal Action Segmentation
Authors: Qing Zhong, Guodong Ding, Angela Yao
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 24,262 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,369 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On three common segmentation benchmarks, our approach achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Qing Zhong1,2 Guodong Ding2 Angela Yao2 1University of Adelaide 2National University of Singapore EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Adaptive Memory Update. ... Algorithm 2 Post-processing for Online TAS. |
| Open Source Code | No | The code will be made available once accepted. |
| Open Datasets | Yes | We evaluate our model on three common TAS datasets. Breakfast [18] comprises in total 1,712 videos... 50Salads [38] has 50 videos... GTEA [12] contains 28 videos... |
| Dataset Splits | Yes | We evaluate our model on three common TAS datasets. ... We train the model end-to-end with a learning rate of 5e 4 of total 50 epochs. Detailed hyperparameter settings can be found in Appendix. |
| Hardware Specification | Yes | We evaluate the runtime performance of our approach using an Nvidia A40 GPU... A single NVIDIA RTX 3090 GPU is sufficient for training. |
| Software Dependencies | No | The paper mentions several architectural components (e.g., GRU, Transformer, Swin, TCN) and implies the use of common machine learning libraries, but it does not specify software dependencies with version numbers (e.g., 'PyTorch 1.9' or 'CUDA 11.1'). |
| Experiment Setup | Yes | We train the model end-to-end with a learning rate of 5e 4 of total 50 epochs. Detailed hyperparameter settings can be found in Appendix. ... Table 11: Hyper-parameter settings for GTEA, 50Salads, and Breakfast datasets. |