Web: http://arxiv.org/abs/2101.07253

June 23, 2022, 1:13 a.m. | Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, Patrick Pérez

cs.CV updates on arXiv.org arxiv.org

Domain adaptation is an important task to enable learning when labels are
scarce. While most works focus only on the image modality, there are many
important multi-modal datasets. In order to leverage multi-modality for domain
adaptation, we propose cross-modal learning, where we enforce consistency
between the predictions of two modalities via mutual mimicking. We constrain
our network to make correct predictions on labeled data and consistent
predictions across modalities on unlabeled target-domain data. Experiments in
unsupervised and semi-supervised domain adaptation …

3d arxiv cross cv domain adaptation learning segmentation semantic

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