3 research outputs found
Unsupervised Scene Adaptation with Memory Regularization in vivo
We consider the unsupervised scene adaptation problem of learning from both
labeled source data and unlabeled target data. Existing methods focus on
minoring the inter-domain gap between the source and target domains. However,
the intra-domain knowledge and inherent uncertainty learned by the network are
under-explored. In this paper, we propose an orthogonal method, called memory
regularization in vivo to exploit the intra-domain knowledge and regularize the
model training. Specifically, we refer to the segmentation model itself as the
memory module, and minor the discrepancy of the two classifiers, i.e., the
primary classifier and the auxiliary classifier, to reduce the prediction
inconsistency. Without extra parameters, the proposed method is complementary
to the most existing domain adaptation methods and could generally improve the
performance of existing methods. Albeit simple, we verify the effectiveness of
memory regularization on two synthetic-to-real benchmarks: GTA5 -> Cityscapes
and SYNTHIA -> Cityscapes, yielding +11.1% and +11.3% mIoU improvement over the
baseline model, respectively. Besides, a similar +12.0% mIoU improvement is
observed on the cross-city benchmark: Cityscapes -> Oxford RobotCar.Comment: 7 pages, 4 figures, 6 table
DACS: Domain Adaptation via Cross-domain Mixed Sampling
Semantic segmentation models based on convolutional neural networks have
recently displayed remarkable performance for a multitude of applications.
However, these models typically do not generalize well when applied on new
domains, especially when going from synthetic to real data. In this paper we
address the problem of unsupervised domain adaptation (UDA), which attempts to
train on labelled data from one domain (source domain), and simultaneously
learn from unlabelled data in the domain of interest (target domain). Existing
methods have seen success by training on pseudo-labels for these unlabelled
images. Multiple techniques have been proposed to mitigate low-quality
pseudo-labels arising from the domain shift, with varying degrees of success.
We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes
images from the two domains along with the corresponding labels and
pseudo-labels. These mixed samples are then trained on, in addition to the
labelled data itself. We demonstrate the effectiveness of our solution by
achieving state-of-the-art results for GTA5 to Cityscapes, a common
synthetic-to-real semantic segmentation benchmark for UDA.Comment: This paper has been accepted to WACV202