UniMTS: Unified Pre-training for Motion Time Series
Authors: Xiyuan Zhang, Diyan Teng, Ranak Roy Chowdhury, Shuheng Li, Dezhi Hong, Rajesh Gupta, Jingbo Shang
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We evaluate on the most extensive motion time series classification benchmark to date, comprising 18 real-world datasets that cover diverse activities. |
| Researcher Affiliation | Collaboration | Xiyuan Zhang UC San Diego EMAIL Diyan Teng Qualcomm EMAIL Ranak Roy Chowdhury UC San Diego EMAIL Shuheng Li UC San Diego EMAIL Dezhi Hong Amazon EMAIL Rajesh K. Gupta UC San Diego EMAIL Jingbo Shang UC San Diego EMAIL |
| Pseudocode | No | Not found. The paper describes processes and frameworks but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code is available on Github: https://github.com/xiyuanzh/Uni MTS. Model is available on Hugging Face: https://huggingface.co/xiyuanz/Uni MTS. |
| Open Datasets | Yes | We simulate motion time series from existing motion skeleton dataset Human ML3D [19], which contain both motion skeleton data and corresponding text descriptions as detailed in Section A.1 in Appendix. |
| Dataset Splits | No | Not found. The paper discusses train, few-shot, and zero-shot settings but does not explicitly specify a validation dataset split or how it was used. |
| Hardware Specification | Yes | We pre-train Uni MTS using Adam optimizer [25] with a learning rate of 0.0001 on a single NVIDIA A100 GPU. |
| Software Dependencies | Yes | We prompt GPT-3.5 ( gpt-3.5-turbo ) to generate k = 3 paraphrases. |
| Experiment Setup | Yes | We pre-train Uni MTS using Adam optimizer [25] with a learning rate of 0.0001 on a single NVIDIA A100 GPU. The pre-training process consumes approximately 13 GB of memory given a batch size of 64. For text augmentation, we prompt GPT-3.5 ( gpt-3.5-turbo ) to generate k = 3 paraphrases. During each iteration, we randomly generate the mask M by selecting 1 to 5 joints and mask the remaining joints as zeros. We adopt learnable temperature parameter γ initialized from CLIP. |