Segda: Maximum separable segment mask with pseudo labels for domain adaptive semantic segmentation
A Khandelwal - Proceedings of the IEEE/CVF International …, 2023 - openaccess.thecvf.com
Abstract Unsupervised Domain Adaptation (UDA) aims to solve the problem of label scarcity
of the target domain by transferring the knowledge from the label rich source domain …
of the target domain by transferring the knowledge from the label rich source domain …
Open-Set Domain Adaptation for Semantic Segmentation
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-
wise knowledge from the labeled source domain to the unlabeled target domain. However …
wise knowledge from the labeled source domain to the unlabeled target domain. However …
Prototypical contrast adaptation for domain adaptive semantic segmentation
Abstract Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the
labeled source domain to an unlabeled target domain. In this paper, we present Prototypical …
labeled source domain to an unlabeled target domain. In this paper, we present Prototypical …
Consistency regularization for domain adaptation
KB Koh, B Fernando - European Conference on Computer Vision, 2022 - Springer
Collection of real world annotations for training semantic segmentation models is an
expensive process. Unsupervised domain adaptation (UDA) tries to solve this problem by …
expensive process. Unsupervised domain adaptation (UDA) tries to solve this problem by …
Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation
As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …
costly process, a model can instead be trained with more accessible synthetic data and …
Adaptive refining-aggregation-separation framework for unsupervised domain adaptation semantic segmentation
Unsupervised domain adaptation has attracted widespread attention as a promising method
to solve the labeling difficulties of semantic segmentation tasks. It trains a segmentation …
to solve the labeling difficulties of semantic segmentation tasks. It trains a segmentation …
Dsp: Dual soft-paste for unsupervised domain adaptive semantic segmentation
Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a
segmentation model trained on the labeled source domain to the unlabeled target domain …
segmentation model trained on the labeled source domain to the unlabeled target domain …
Labor: Labeling only if required for domain adaptive semantic segmentation
Abstract Unsupervised Domain Adaptation (UDA) for semantic segmentation has been
actively studied to mitigate the domain gap between label-rich source data and unlabeled …
actively studied to mitigate the domain gap between label-rich source data and unlabeled …
Cluster alignment with target knowledge mining for unsupervised domain adaptation semantic segmentation
Unsupervised domain adaptation (UDA) carries out knowledge transfer from the labeled
source domain to the unlabeled target domain. Existing feature alignment methods in UDA …
source domain to the unlabeled target domain. Existing feature alignment methods in UDA …
Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation
Self-training is a competitive approach in domain adaptive segmentation, which trains the
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …