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Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation
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<span title="2021-06-23">2021</span>
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arXiv
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To alleviate this, in this work, we target source free UDA for segmentation, and propose to adapt an "off-the-shelf" segmentation model pre-trained in the source domain to the target domain, with an adaptive ...
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled and unseen target domain, which is usually trained on data from both domains. ...
We minimize the domain discrepancy based on the adaptively computed batch-wise statistics in each channel. ...
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Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
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<span title="2022-04-20">2022</span>
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arXiv
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Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. ...
This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. ...
Related Work
Unsupervised Domain Adaptation Unsupervised Domain Adaptation (UDA) has been widely used for biomedical image segmentation tasks. ...
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Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation
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<span title="2022-05-28">2022</span>
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arXiv
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Furthermore, the proposed approach is proven to be effective for source-free unsupervised domain adaptation in reverse direction. ...
In the first stage, the feature map statistics-guided model adaptation combined with entropy minimization is developed to help the top segmentation network reliably segment the target images. ...
In the medical image analysis field, Chen et al. (2021) proposed a denoised pseudo-labeling strategy for source-free unsupervised domain adaptation in fundus image segmentation. ...
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Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
[article]
<span title="2021-10-26">2021</span>
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arXiv
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the segmentation probability maps of the source domain image. ...
Consequently, there is a need for methods that generalize well despite restricted access to target domain data during training. ...
Unsupervised Domain Adaptation Unsupervised domain adaptation (UDA) is a subset of transfer learning. ...
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Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
[article]
<span title="2021-08-24">2021</span>
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arXiv
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Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains. ...
Our criterion is simpler than competing validation methods, yet more effective; it can tune hyper-parameters and the number of training iterations in both image classification and semantic segmentation ...
Empirically, we observe that SND works well for closed and partial domain adaptive image classification, as well as for domain adaptive semantic segmentation. ...
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FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
[article]
<span title="2016-12-08">2016</span>
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arXiv
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In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. ...
Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. ...
In this work, we propose the first unsupervised domain adaptation method for transferring semantic segmentation FCNs across image domains. ...
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Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization
[article]
<span title="2021-08-04">2021</span>
<i >
arXiv
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In this work, we explore unsupervised domain adaptation in retinal vessel segmentation by using entropy-based adversarial learning and transfer normalization layer to train a segmentation network, which ...
minimization on the target domain. ...
Fig. 2 . 2 The overview of our proposed framework for unsupervised domain adaptation in retinal vessel segmentation. ...
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Fully Convolutional Adaptation Networks for Semantic Segmentation
[article]
<span title="2018-04-23">2018</span>
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arXiv
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The former adapts source-domain images to appear as if drawn from the "style" in the target domain and the latter attempts to learn domain-invariant representations. ...
Specifically, we present Fully Convolutional Adaptation Networks (FCAN), a novel deep architecture for semantic segmentation which combines Appearance Adaptation Networks (AAN) and Representation Adaptation ...
In short, our work in this paper mainly focuses on unsupervised adaptation for semantic segmentation task, which is seldom investigated. ...
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Fully Convolutional Adaptation Networks for Semantic Segmentation
<span title="">2018</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</a>
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The former adapts source-domain images to appear as if drawn from the "style" in the target domain and the latter attempts to learn domain-invariant represen- tations. ...
Specifically, we present Fully Convolutional Adaptation Networks (FCAN), a novel deep architecture for semantic segmentation which combines Appearance Adaptation Networks (AAN) and Representation Adaptation ...
In short, our work in this paper mainly focuses on unsupervised adaptation for semantic segmentation task, which is seldom investigated. ...
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Unsupervised Domain Adaptation with Implicit Pseudo Supervision for Semantic Segmentation
[article]
<span title="2022-04-14">2022</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Pseudo-labelling is a popular technique in unsuper-vised domain adaptation for semantic segmentation. ...
To further implicitly utilize the pseudo labels, we maximize the distances of features for different classes and minimize the distances for the same classes by triplet loss. ...
ACKNOWLEDGMENT Sincere gratitude to anonymous reviewers for careful work and considerate suggestions. ...
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Towards Adaptive Semantic Segmentation by Progressive Feature Refinement
[article]
<span title="2020-09-30">2020</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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Unsupervised adaptive semantic segmentation aims to obtain a robust classifier trained with source domain data, which is able to maintain stable performance when deployed to a target domain with different ...
As a result, the segmentation models trained with source domain images can be transferred to a target domain without significant performance degradation. ...
Unsupervised domain adaptation offers a formal framework for addressing the above-mentioned issues by bridging the domain gap between the source and target domains. ...
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Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
[article]
<span title="2020-10-15">2020</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2020-03-08">2020</span>
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<span title="2020-04-14">2020</span>
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This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation. ...
To overcome the problem, this paper proposes to explicitly estimate the prediction uncertainty during training to rectify the pseudo label learning for unsupervised semantic segmentation adaptation. ...
In unsupervised semantic segmentation adaptation, two datasets collected in different environments are considered: a labeled source-domain dataset where category labels are provided for every pixel, and ...
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Dispensed Transformer Network for Unsupervised Domain Adaptation
[article]
<span title="2021-10-28">2021</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. ...
Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. ...
This research mainly involves three fields: medical image segmentation, unsupervised domain adaptation, and transformer in vision. ...
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Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation
[article]
<span title="2021-06-23">2021</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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Extensive experiments have been performed on domain adaptive semantic segmentation benchmark, GTA5 → Cityscapes. ...
Aiming at these pitfalls, this study develops a domain adaptive solution to semantic segmentation with pseudo label rectification (namely PR-SFDA), which operates in two phases: 1) Confidence-regularized ...
Self-training for Domain Adaptive Segmentation. ...
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Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation
[article]
<span title="2021-09-30">2021</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2022-02-28">2022</span>
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Unsupervised Domain Adaptation (UDA) techniques are thus essential to fill this domain gap and retain the performance of models on new sensor setups without the need for additional data labeling. ...
In this paper, we propose AdaptLPS, a novel UDA approach for LiDAR panoptic segmentation that leverages task-specific knowledge and accounts for variation in the number of scan lines, mounting position ...
CONCLUSION In this paper, we present the first end-to-end trainable unsupervised domain adaptation approach for LiDAR panoptic segmentation. ...
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