VMA: Domain Variance- and Modality-Aware Model Transfer for Fine-Grained Occupant Activity Recognition

Author:

Hu Zhizhang1,Zhang Yue1,Yu Tong2,Pan Shijia1

Affiliation:

1. Univ. of California Merced,Merced,California,USA

2. Adobe Research,San Jose,California,USA

Funder

CITRIS

Publisher

IEEE

Reference42 articles.

1. Hybrid Cross Deep Network for Domain Adaptation and Energy Saving in Visual Internet of Things

2. All-cause mortality risk in elderly individuals with disabilities: a retrospective observational study;li-wei;BMJ Open,2016

3. Dropout: a simple way to prevent neural networks from overfitting;srivastava;The Journal of Machine Learning Research,2014

4. Unsupervised domain adaptation for activity recognition across heterogeneous datasets

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1. FreqMAE: Frequency-Aware Masked Autoencoder for Multi-Modal IoT Sensing;Proceedings of the ACM Web Conference 2024;2024-05-13

2. IOTeeth;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-03-06

3. Poster Abstract: Integrating On- and Off-body Sensing for Young Adults Failure to Launch (FTL) Behavior Profiling;The 22nd International Conference on Information Processing in Sensor Networks;2023-05-09

4. CMA: Cross-Modal Association Between Wearable and Structural Vibration Signal Segments for Indoor Occupant Sensing;The 22nd International Conference on Information Processing in Sensor Networks;2023-05-09

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