Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings

• URL: http://arxiv.org/abs/2011.12616v1
• Date: Wed, 25 Nov 2020 10:06:22 GMT
• Title: Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings
• Authors: Marco Toldo, Umberto Michieli, Pietro Zanuttigh
• Abstract summary: We propose an effective Unsupervised Domain Adaptation (UDA) strategy, based on a feature clustering method. We introduce two novel learning objectives to enhance the discriminative clustering performance.
• Score: 25.137859989323537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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