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Article overview
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Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection | | Fei Pan
; Francois Rameau
; In So Kweon
; | | Date: |
1 Jun 2022 | | Abstract: | Training models dedicated to semantic segmentation requires a large amount of
pixel-wise annotated data. Due to their costly nature, these annotations might
not be available for the task at hand. To alleviate this problem, unsupervised
domain adaptation approaches aim at aligning the feature distributions between
the labeled source and the unlabeled target data. While these strategies lead
to noticeable improvements, their effectiveness remains limited. To guide the
domain adaptation task more efficiently, previous works attempted to include
human interactions in this process under the form of sparse single-pixel
annotations in the target data. In this work, we propose a new domain
adaptation framework for semantic segmentation with annotated points via active
selection. First, we conduct an unsupervised domain adaptation of the model;
from this adaptation, we use an entropy-based uncertainty measurement for
target points selection. Finally, to minimize the domain gap, we propose a
domain adaptation framework utilizing these target points annotated by human
annotators. Experimental results on benchmark datasets show the effectiveness
of our methods against existing unsupervised domain adaptation approaches. The
propose pipeline is generic and can be included as an extra module to existing
domain adaptation strategies. | | Source: | arXiv, 2206.00181 | | Services: | Forum | Review | PDF | Favorites | |
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