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 | Conference 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 xiyuanzh@ucsd.edu Diyan Teng Qualcomm diyateng@qti.qualcomm.com Ranak Roy Chowdhury UC San Diego rrchowdh@ucsd.edu Shuheng Li UC San Diego shl060@ucsd.edu Dezhi Hong Amazon hondezhi@amazon.com Rajesh K. Gupta UC San Diego rgupta@ucsd.edu Jingbo Shang UC San Diego jshang@ucsd.edu |
| 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. |