Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification
Authors: Jimmy Lin, Junkai Li, Jiasi Gao, Weizhi Ma, Yang Liu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a public action classification dataset demonstrate that our model outperforms state-of-the-art methods in all metrics. |
| Researcher Affiliation | Academia | 1 Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China 2 Department of Computer Science and Technology, Tsinghua University, Beijing, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Aressfull/sock classification. |
| Open Datasets | Yes | So our experiments are conducted on the public tactile signal dataset1, which is collected by individuals with two wearable electronic socks to perform specific actions. 1http://senstextile.csail.mit.edu/ |
| Dataset Splits | Yes | Following the providers settings, 500 and 1,000 samples of each action are used in validation and testing, respectively, and the other samples are used in training (each action type will be sampled to 4,000 samples). |
| Hardware Specification | Yes | All experiments are implemented by Pytorch 1.7 and executed on 4 Tesla V100 or Ge Force RTX 3090 GPUs. |
| Software Dependencies | Yes | All experiments are implemented by Pytorch 1.7 and executed on 4 Tesla V100 or Ge Force RTX 3090 GPUs. |
| Experiment Setup | Yes | Table 3: Summarization of tuned hyper-parameters. The tubelet parameters L and P are set to 5 and 4, and the pretraining and fine-tuning epoch is set to 60. The embedding dimension D is set to 768, in which batch size is 64 and weight decay is 1e-4. |