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Learning to adapt class-specific features across domains for semantic segmentation
[article]
2020
arXiv
pre-print
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In this thesis, we present a novel architecture, which learns to adapt features across domains by taking into account per class information. ...
Additionally, the feature adaptation step often happens globally, at a coarse level, hindering its applicability to tasks such as semantic segmentation, where details are of crucial importance to provide ...
CONCLUSIONS In this thesis, we have tackled the unsupervised domain adaptation problem for semantic segmentation. ...
arXiv:2001.08311v1
fatcat:xeqowbv2bzb5vaitwfuqszlj4a
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation
[article]
2021
arXiv
pre-print
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We propose a novel meta-learning scheme with feature disentanglement ability, which derives domain-invariant features for semantic segmentation with domain generalization guarantees. ...
How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities. ...
By advancing meta-learning strategies for [13] , we apply feature disentanglement models to learn domain-invariant content features across multiple source domains. ...
arXiv:2112.13538v1
fatcat:qsmcpc5d7vc43kv3fzevyh7abu
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
[article]
2015
arXiv
pre-print
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Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations available for different categories to guide segmentations on images with only image-level class ...
To make the segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. ...
The attention model provides not only predictions for localization but also category-specific information that enables us to adapt the decoder trained in source domain to target domain. ...
arXiv:1512.07928v1
fatcat:p6kdgj7gbvdrrgxzrdhss275k4
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations available for different categories to guide segmentations on images with only image-level class ...
To make segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. ...
The attention model provides not only predictions for localization but also category-specific information that enables us to adapt the decoder trained in source domain to target domain. ...
doi:10.1109/cvpr.2016.349
dblp:conf/cvpr/HongOLH16
fatcat:obemojsyvvfflodeaxd3ptfyse
Distribution regularized self-supervised learning for domain adaptation of semantic segmentation
2022
Image and Vision Computing
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2022 pre-print arXiv:2206.09683v1 fatcat:d4ivo5nczjgw3c5uwo6wonzksi
This paper proposes a novel pixel-level distribution regularization scheme (DRSL) for self-supervised domain adaptation of semantic segmentation. ...
In a typical setting, the classification loss forces the semantic segmentation model to greedily learn the representations that capture inter-class variations in order to determine the decision (class) ...
Our work is related to semantic segmentation, domain adaptation, and self-supervised learning. ...
doi:10.1016/j.imavis.2022.104504
fatcat:dtn6qgbr4jayfecrvm6mzko3sa
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
[article]
2016
arXiv
pre-print
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Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. ...
In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. ...
In this work, we propose the first unsupervised domain adaptation method for transferring semantic segmentation FCNs across image domains. ...
arXiv:1612.02649v1
fatcat:3ku35eodhvcwxpboh4hrggnq3y
Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation
[article]
2020
arXiv
pre-print
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Recent advances in unsupervised domain adaptation for semantic segmentation have shown great potentials to relieve the demand of expensive per-pixel annotations. ...
Specifically, CrCDA learns and enforces the prototypical local contextual-relations explicitly in the feature space of a labelled source domain while transferring them to an unlabelled target domain via ...
[19] first applies adversarial learning for UDA based semantic segmentation by aligning feature space at global scale. ...
arXiv:2007.02424v2
fatcat:kby6leqi2zdkrniapzwrtnlxny
Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels
[article]
2022
arXiv
pre-print
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Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. ...
In this work, we present Cluster-to-Adapt (C2A), a computationally efficient clustering-based approach for domain adaptation across segmentation datasets with completely different, but possibly related ...
Similar to any semantic segmentation task, the label distribution is not uniform across all the classes in the images. ...
arXiv:2208.02804v1
fatcat:uvz7a5ascjhdjktwyhzp7bx5a4
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
[article]
2017
arXiv
pre-print
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By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction ...
Instead of collecting a large number of annotated images of each city of interest to train or refine the segmenter, we propose an unsupervised learning approach to adapt road scene segmenters across different ...
Thus, how to extend the idea of domain adversarial learning for adapting segmenters across image domains would be our focus. ...
arXiv:1704.08509v1
fatcat:7ldqict6yzba5cm6j2aqsdqcu4
Semantic Domain Adversarial Networks for Unsupervised Domain Adaptation
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2003.13274v2 fatcat:btscnrcagfb2hinqb4hkzfjbreOther Versions
Experiments on both object recognition and semantic segmentation show that SDAN effectively aligns the multi-modal structures across domains and even outperforms state-of-the-art domain adversarial training ...
To successfully align the multi-modal data structures across domains, the following works exploit discriminative information in the adversarial training process, e.g., using multiple class-wise discriminators ...
Different from class-agnostic adversarial learning that pursues the marginal distribution alignment but ignores the semantic consistency across domains, (a) previous conditional adversarial learning methods ...
arXiv:2003.13274v3
fatcat:yuwcf73ksbc3tfkv4dmnmaqk44
Affinity Space Adaptation for Semantic Segmentation Across Domains
2020
IEEE Transactions on Image Processing
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2020 pre-print arXiv:2009.12559v1 fatcat:atnp7b5nqnc7liqum5a4c4u4zi
Semantic segmentation with dense pixel-wise annotation has achieved excellent performance thanks to deep learning. ...
Extensive experiments demonstrate that the proposed method achieves superior performance against some state-of-theart methods on several challenging benchmarks for semantic segmentation across domains. ...
Similar to adaptation on classification, most modern adaptation models for semantic segmentation also rely on adversarial learning to adapt domains either in intermediate feature level [11] , [39] , ...
doi:10.1109/tip.2020.3018221
pmid:32870790
fatcat:vb55cxi645fubnnql2i24cqsji
Context-Aware Domain Adaptation in Semantic Segmentation
[article]
2020
arXiv
pre-print
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., what and how to transfer domain knowledge across two domains. Existing methods mainly focus on adapting domain-invariant features (what to transfer) through adversarial learning (how to transfer). ...
In this paper, we consider the problem of unsupervised domain adaptation in the semantic segmentation. ...
[13] propose the first domain adaptation model for semantic segmentation by learning domain-invariant features through adversarial training. ...
arXiv:2003.04010v1
fatcat:h3eyymo5abd6vahm3xsf5io2ui
SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning
[article]
2022
arXiv
pre-print
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Specifically, the semantic prototypes provide supervisory signals for per-pixel discriminative representation learning and each pixel of source and target domains in the feature space is required to reflect ...
To solve this issue, we propose a novel semantic prototype-based contrastive learning framework for fine-grained class alignment. ...
Specifically, to design an effective contrastive loss for domain adaptive semantic segmentation, the key is to construct the appropriate query-key pairs. ...
arXiv:2111.12358v2
fatcat:tiataqjujfdifhpe6y274giv3m
TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays
[article]
2019
arXiv
pre-print
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In this work, we exploit the unsupervised domain adaptation problem for radiology image interpretation across domains. ...
To address the shortcoming of cross-domain, unpaired image-to-image translation methods which typically ignore class-specific semantics, we propose a task-driven, discriminatively trained, cycle-consistent ...
The authors thank NVIDIA for GPU donations. ...
arXiv:1908.07926v1
fatcat:h2frvrescffr7m772dxjnhckom
Universal Semi-Supervised Semantic Segmentation
[article]
2019
arXiv
pre-print
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In recent years, the need for semantic segmentation has arisen across several different applications and environments. ...
In contrast to counterpoints such as fine tuning, joint training or unsupervised domain adaptation, universal semi-supervised segmentation ensures that across all domains: (i) a single model is deployed ...
class prototypes [59] for performing semantic transfer across domains. ...
arXiv:1811.10323v3
fatcat:gzblvf2f4ndndamvpoxda57asu
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