Summary of Primus: Pretraining Imu Encoders with Multimodal Self-supervision, by Arnav M. Das et al.
PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision
by Arnav M. Das, Chi Ian Tang, Fahim Kawsar, Mohammad Malekzadeh
First submitted to arxiv on: 22 Nov 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 paper proposes a method called PRIMUS (PRetraining IMU encoderS) that uses a novel pretraining objective to improve the performance of inertial measurement unit (IMU) encoders. The proposed approach combines self-supervision, multimodal, and nearest-neighbor supervision to enhance downstream performance on both in-domain and out-of-domain datasets. The method is empirically validated based on test accuracy improvements of up to 15% compared to state-of-the-art baselines using fewer than 500 labeled samples per class. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to make devices that can sense human motions better. It uses special sensors called IMUs and a new approach called PRIMUS to make it work. This helps improve the accuracy of predicting human movements from data collected by these sensors. The results show that this method works well, even when there’s not much labeled data available. |
Keywords
» Artificial intelligence » Nearest neighbor » Pretraining
