This study presents an unsupervised alignment architecture using a supervised learning model to align heterogeneous data with unknown semantic time shifts. It demonstrates effectiveness in aligning optical and acoustic signals, enabling semantic mining and information fusion with minimal domain knowledge.
- Chaofan Li
- Zhichao Ma
- Luquan Ren
