arXiv Machine Learning By Ahmed Mohamady, Robin Burchard, Kristof Van Laerhoven

A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset

Read the original on arXiv Machine Learning →

arXiv:2606. 27886v1 Announce Type: new Abstract: Recent advances in Human Activity Recognition (HAR) from wearable sensors have shown that multi-modal deep learning models consistently outperform their uni-modal counterparts.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Hugging Face Trending Papers
Jul 13

LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments

This paper presents a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework for action and activity recognition in healthcare-oriented training environments. The proposed architecture combines parameter-efficient modality-specific adaptation with sequential fusion, enabling modalities to be integrated in stages without retraining previously learned components.