Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings

Marco Toldo; Umberto Michieli; Pietro Zanuttigh

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arXiv · 2011.12616

Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings

Marco Toldo·Umberto Michieli·Pietro Zanuttigh

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Abstract

Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adaptation techniques able to transfer learned knowledge from label-abundant domains to unlabeled ones. In this paper we propose an effective Unsupervised Domain Adaptation (UDA) strategy, based on a feature clustering method that captures the different semantic modes of the feature distribution and groups features of the same class into tight and well-separated clusters. Furthermore, we introduce two novel learning objectives to enhance the discriminative clustering performance: an orthogonality loss forces spaced out individual representations to be orthogonal, while a sparsity loss reduces class-wise the number of active feature channels. The joint effect of these modules is to regularize the structure of the feature space. Extensive evaluations in the synthetic-to-real scenario show that we achieve state-of-the-art performance.

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BibTeX RIS

Marco Toldo, Umberto Michieli, Pietro Zanuttigh. 2020-11-25. Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings. https://arxiv.org/abs/2011.12616

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