FDA: Fourier Domain Adaptation for Semantic Segmentation
- URL: http://arxiv.org/abs/2004.05498v1
- Date: Sat, 11 Apr 2020 22:20:48 GMT
- Title: FDA: Fourier Domain Adaptation for Semantic Segmentation
- Authors: Yanchao Yang and Stefano Soatto
- Abstract summary: We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other.
We illustrate the method in semantic segmentation, where densely annotated images are aplenty in one domain, but difficult to obtain in another.
Our results indicate that even simple procedures can discount nuisance variability in the data that more sophisticated methods struggle to learn away.
- Score: 82.4963423086097
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We describe a simple method for unsupervised domain adaptation, whereby the
discrepancy between the source and target distributions is reduced by swapping
the low-frequency spectrum of one with the other. We illustrate the method in
semantic segmentation, where densely annotated images are aplenty in one domain
(synthetic data), but difficult to obtain in another (real images). Current
state-of-the-art methods are complex, some requiring adversarial optimization
to render the backbone of a neural network invariant to the discrete domain
selection variable. Our method does not require any training to perform the
domain alignment, just a simple Fourier Transform and its inverse. Despite its
simplicity, it achieves state-of-the-art performance in the current benchmarks,
when integrated into a relatively standard semantic segmentation model. Our
results indicate that even simple procedures can discount nuisance variability
in the data that more sophisticated methods struggle to learn away.
Related papers
- Unsupervised Domain Adaptation for Semantic Segmentation using One-shot
Image-to-Image Translation via Latent Representation Mixing [9.118706387430883]
We propose a new unsupervised domain adaptation method for the semantic segmentation of very high resolution images.
An image-to-image translation paradigm is proposed, based on an encoder-decoder principle where latent content representations are mixed across domains.
Cross-city comparative experiments have shown that the proposed method outperforms state-of-the-art domain adaptation methods.
arXiv Detail & Related papers (2022-12-07T18:16:17Z) - Semi-supervised domain adaptation with CycleGAN guided by a downstream
task loss [4.941630596191806]
Domain adaptation is of huge interest as labeling is an expensive and error-prone task.
Image-to-image approaches can be used to mitigate the shift in the input.
We propose a "task aware" version of a GAN in an image-to-image domain adaptation approach.
arXiv Detail & Related papers (2022-08-18T13:13:30Z) - Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging [2.024988885579277]
Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets.
We propose a very light and transparent approach to perform test-time domain adaptation.
arXiv Detail & Related papers (2022-07-31T17:28:42Z) - Learning Instance-Specific Adaptation for Cross-Domain Segmentation [79.61787982393238]
We propose a test-time adaptation method for cross-domain image segmentation.
Given a new unseen instance at test time, we adapt a pre-trained model by conducting instance-specific BatchNorm calibration.
arXiv Detail & Related papers (2022-03-30T17:59:45Z) - AFAN: Augmented Feature Alignment Network for Cross-Domain Object
Detection [90.18752912204778]
Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications.
We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training.
Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
arXiv Detail & Related papers (2021-06-10T05:01:20Z) - Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive
Learning [62.7588467386166]
We leverage contrastive learning to bridge the domain gap by aligning the features of structurally similar label patches across domains.
Our approach consistently outperforms state-of-the-art unsupervised and semi-supervised methods on two challenging domain adaptive segmentation tasks.
arXiv Detail & Related papers (2021-04-22T13:39:12Z) - Pixel-Level Cycle Association: A New Perspective for Domain Adaptive
Semantic Segmentation [169.82760468633236]
We propose to build the pixel-level cycle association between source and target pixel pairs.
Our method can be trained end-to-end in one stage and introduces no additional parameters.
arXiv Detail & Related papers (2020-10-31T00:11:36Z) - Semantically Adaptive Image-to-image Translation for Domain Adaptation
of Semantic Segmentation [1.8275108630751844]
We address the problem of domain adaptation for semantic segmentation of street scenes.
Many state-of-the-art approaches focus on translating the source image while imposing that the result should be semantically consistent with the input.
We advocate that the image semantics can also be exploited to guide the translation algorithm.
arXiv Detail & Related papers (2020-09-02T16:16:50Z) - Keep it Simple: Image Statistics Matching for Domain Adaptation [0.0]
Domain Adaptation (DA) is a technique to maintain detection accuracy when only unlabeled images are available of the target domain.
Recent state-of-the-art methods try to reduce the domain gap using an adversarial training strategy.
We propose to align either color histograms or mean and covariance of the source images towards the target domain.
In comparison to recent methods, we achieve state-of-the-art performance using a much simpler procedure for the training.
arXiv Detail & Related papers (2020-05-26T07:32:09Z) - Unsupervised Intra-domain Adaptation for Semantic Segmentation through
Self-Supervision [73.76277367528657]
Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation.
To cope with this limitation, automatically annotated data generated from graphic engines are used to train segmentation models.
We propose a two-step self-supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together.
arXiv Detail & Related papers (2020-04-16T15:24:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.