MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning

• URL: http://arxiv.org/abs/2408.04243v1
• Date: Thu, 8 Aug 2024 06:16:00 GMT
• Title: MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning
• Authors: Rex Liu, Xin Liu,
• Abstract summary: We introduce Multimodal Masked Autoenco-Based One-Shot Learning (Mu-MAE) Mu-MAE integrates a multimodal masked autoencoder with a synchronized masking strategy tailored for wearable sensors. It achieves up to an 80.17% accuracy five-way one-shot multimodal classification for classification without the use of additional data.
• Score: 3.520960737058199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the exponential growth of multimedia data, leveraging multimodal sensors presents a promising approach for improving accuracy in human activity recognition. Nevertheless, accurately identifying these activities using both video data and wearable sensor data presents challenges due to the labor-intensive data annotation, and reliance on external pretrained models or additional data. To address these challenges, we introduce Multimodal Masked Autoencoders-Based One-Shot Learning (Mu-MAE). Mu-MAE integrates a multimodal masked autoencoder with a synchronized masking strategy tailored for wearable sensors. This masking strategy compels the networks to capture more meaningful spatiotemporal features, which enables effective self-supervised pretraining without the need for external data. Furthermore, Mu-MAE leverages the representation extracted from multimodal masked autoencoders as prior information input to a cross-attention multimodal fusion layer. This fusion layer emphasizes spatiotemporal features requiring attention across different modalities while highlighting differences from other classes, aiding in the classification of various classes in metric-based one-shot learning. Comprehensive evaluations on MMAct one-shot classification show that Mu-MAE outperforms all the evaluated approaches, achieving up to an 80.17% accuracy for five-way one-shot multimodal classification, without the use of additional data.

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