most citedFOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space

8 citations · 8 across the 1 of their papers we have counts for

collaborators

1 paper

cs.AI2023★ 8 cited

FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space

Shengzhong Liu, Tomoyoshi Kimura, Dongxin Liu +5

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting comprehensive features from multimodal time-series sensing signals through self-supervised…