Summary of Mesen: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels, by Lilin Xu et al.
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels
by Lilin Xu, Chaojie Gu, Rui Tan, Shibo He, Jiming Chen
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, MESEN, is a multimodal-empowered unimodal sensing approach designed to utilize unlabeled multimodal data for enhancing unimodal human activity recognition (HAR). During the model design phase, MESEN uses multimodal fusion and pre-training with a multi-task mechanism to extract effective unimodal features. This framework demonstrates significant performance improvements over state-of-the-art baselines in enhancing unimodal HAR by exploiting multimodal data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MESEN is a new way to use lots of different types of data together to make better machines that can recognize human activities. It takes some extra information from these different types of data and uses it to make the machine’s job easier when it’s actually doing the activity recognition. This helps the machine do its job better even with only a little bit of labeled information. |
Keywords
* Artificial intelligence * Activity recognition * Multi task
