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MIC: Masked image consistency for context-enhanced domain adaptation
In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …
adapted to target data (eg real-world) without access to target annotation. Most previous …
Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation
Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on
an unlabeled target domain by utilizing the supervised model trained on a labeled source …
an unlabeled target domain by utilizing the supervised model trained on a labeled source …
Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …
target domain, but it requires to access the source data which often raises concerns in data …
Polarmix: A general data augmentation technique for lidar point clouds
LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously,
capture precise geometry of the surrounding environment and are crucial to many …
capture precise geometry of the surrounding environment and are crucial to many …
3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds
Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …
Aloft: A lightweight mlp-like architecture with dynamic low-frequency transform for domain generalization
Abstract Domain generalization (DG) aims to learn a model that generalizes well to unseen
target domains utilizing multiple source domains without re-training. Most existing DG works …
target domains utilizing multiple source domains without re-training. Most existing DG works …
Eventdance: Unsupervised source-free cross-modal adaptation for event-based object recognition
In this paper we make the first attempt at achieving the cross-modal (ie image-to-events)
adaptation for event-based object recognition without accessing any labeled source image …
adaptation for event-based object recognition without accessing any labeled source image …
Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation
Abstract Traditional Unsupervised Domain Adaptation (UDA) leverages the labeled source
domain to tackle the learning tasks on the unlabeled target domain. It can be more …
domain to tackle the learning tasks on the unlabeled target domain. It can be more …
Deformation depth decoupling network for point cloud domain adaptation
Recently, point cloud domain adaptation (DA) practices have been implemented to improve
the generalization ability of deep learning models on point cloud data. However, variations …
the generalization ability of deep learning models on point cloud data. However, variations …
Pipa: Pixel-and patch-wise self-supervised learning for domain adaptative semantic segmentation
Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned
model to other domains. The domain-invariant knowledge is transferred from the model …
model to other domains. The domain-invariant knowledge is transferred from the model …