Title: Heterogeneous-Modal Unsupervised Domain Adaptation via Latent Space Bridging
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Transfer learning, Domain adaptation, Heterogeneous-modal unsupervised domain adaptation

Abstract: Unsupervised domain adaptation (UDA) methods effectively bridge domain gaps but become struggled when the source and target domains belong to entirely distinct modalities. To address this limitation, we propose a novel setting called Heterogeneous-Modal Unsupervised Domain Adaptation (HMUDA), which enables knowledge transfer between completely different modalities by leveraging a bridge domain containing unlabeled samples from both modalities. 
To learn under the HMUDA setting, we propose Latent Space Bridging (LSB), a specialized framework designed for the semantic segmentation task. 
Specifically, LSB utilizes a dual-branch architecture, incorporating a feature consistency loss to align representations across modalities and a domain alignment loss to reduce discrepancies between class centroids across domains. 
Extensive experiments conducted on six benchmark datasets demonstrate that LSB achieves state-of-the-art performance.