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arXiv AI By Jiawen Yang, Shuhao Chen, Shengtao Zhang, Ke Tang, Yu Zhang

Heterogeneous-Modal Unsupervised Domain Adaptation via Latent Space Bridging

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The paper introduces Heterogeneous-Modal Unsupervised Domain Adaptation (HMUDA), a setting where a labeled source domain and an unlabeled target domain have entirely different modalities, bridged by an unlabeled domain containing paired observations from both modalities. It proposes Latent Space Bridging (LSB), a dual‑branch framework that uses a feature consistency loss on paired bridge samples to close the modality gap and a class‑centroid alignment loss to reduce source‑target discrepancy. Experiments on eight benchmark settings, covering both 2D‑to‑3D and 3D‑to‑2D transfers, show that LSB achieves state‑of‑the‑art performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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