4,348 research outputs found
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
The domain adaptation of satellite images has recently gained an increasing
attention to overcome the limited generalization abilities of machine learning
models when segmenting large-scale satellite images. Most of the existing
approaches seek for adapting the model from one domain to another. However,
such single-source and single-target setting prevents the methods from being
scalable solutions, since nowadays multiple source and target domains having
different data distributions are usually available. Besides, the continuous
proliferation of satellite images necessitates the classifiers to adapt to
continuously increasing data. We propose a novel approach, coined DAugNet, for
unsupervised, multi-source, multi-target, and life-long domain adaptation of
satellite images. It consists of a classifier and a data augmentor. The data
augmentor, which is a shallow network, is able to perform style transfer
between multiple satellite images in an unsupervised manner, even when new data
are added over the time. In each training iteration, it provides the classifier
with diversified data, which makes the classifier robust to large data
distribution difference between the domains. Our extensive experiments prove
that DAugNet significantly better generalizes to new geographic locations than
the existing approaches
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a
significant challenge for recent deep learning models. Unsupervised domain
adaptation (UDA) attempts to solve such problem. Recent works show that
self-training is a powerful approach to UDA. However, existing methods have
difficulty in balancing the scalability and performance. In this paper, we
propose a hard-aware instance adaptive self-training framework for UDA on the
task of semantic segmentation. To effectively improve the quality and diversity
of pseudo-labels, we develop a novel pseudo-label generation strategy with an
instance adaptive selector. We further enrich the hard class pseudo-labels with
inter-image information through a skillfully designed hard-aware pseudo-label
augmentation. Besides, we propose the region-adaptive regularization to smooth
the pseudo-label region and sharpen the non-pseudo-label region. For the
non-pseudo-label region, consistency constraint is also constructed to
introduce stronger supervision signals during model optimization. Our method is
so concise and efficient that it is easy to be generalized to other UDA
methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and
Cityscapes to Oxford RobotCar demonstrate the superior performance of our
approach compared with the state-of-the-art methods.Comment: arXiv admin note: text overlap with arXiv:2008.1219
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