FreqMAE: Frequency-Aware Masked Autoencoder for Multi-Modal IoT Sensing

Kara, Denizhan; Kimura, Tomoyoshi; Liu, Shengzhong; Li, Jinyang; Liu, Dongxin; Wang, Tianshi; Wang, Ruijie; Chen, Yizhuo; Hu, Yigong; Abdelzaher, Tarek

doi:10.1145/3589334.3645346

Proceedings of the ACM Web Conference 2024 2024

DOI: 10.1145/3589334.3645346

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FreqMAE: Frequency-Aware Masked Autoencoder for Multi-Modal IoT Sensing

Denizhan Kara,

Tomoyoshi Kimura,

Shengzhong Liu

et al.

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Cited by 3 publications

References 33 publications

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VibroFM: Towards Micro Foundation Models for Robust Multimodal IoT Sensing

Kimura,

Li,

Wang

et al. 2024

2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS)

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VibroFM: Towards Micro Foundation Models for Robust Multimodal IoT Sensing

Kimura,

Li,

Wang

et al. 2024

2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS)

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Enhancing Resilience in Distributed ML Inference Pipelines for Edge Computing

Wu,

Hanafy,

Souza

et al. 2024

MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM)

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PhyMask: An Adaptive Masking Paradigm for Efficient Self-Supervised Learning in IoT

Kara,

Kimura,

Chen

et al. 2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

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FreqMAE: Frequency-Aware Masked Autoencoder for Multi-Modal IoT Sensing

Cited by 3 publications

References 33 publications

VibroFM: Towards Micro Foundation Models for Robust Multimodal IoT Sensing

VibroFM: Towards Micro Foundation Models for Robust Multimodal IoT Sensing

Enhancing Resilience in Distributed ML Inference Pipelines for Edge Computing

PhyMask: An Adaptive Masking Paradigm for Efficient Self-Supervised Learning in IoT

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