Heterogeneous-Modal Unsupervised Domain Adaptation via Latent Space Bridging

• URL: http://arxiv.org/abs/2506.15971v1
• Date: Thu, 19 Jun 2025 02:31:51 GMT
• Title: Heterogeneous-Modal Unsupervised Domain Adaptation via Latent Space Bridging
• Authors: Jiawen Yang, Shuhao Chen, Yucong Duan, Ke Tang, Yu Zhang,
• Abstract summary: We propose a novel setting called Heterogeneous-Modal Unsupervised Domain Adaptation (HMUDA)<n>HMUDA enables knowledge transfer between completely different modalities by leveraging a bridge domain containing unlabeled samples from both modalities.<n>We propose Latent Space Bridging (LSB), a specialized framework designed for the semantic segmentation task.
• Score: 12.171477896623148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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