Papers One-shot Unsupervised Domain Adaptation
“One-shot Unsupervised Domain Adaptation” 태그가 달린 논문 10편 · 필터 해제
Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to learn discriminative features from a labeled source domain by supervised learning and to transfer the knowledge to an unlabeled target domain via distribution alignment. H…
Contrastive LearningDomain AdaptationFace RecognitionOne-shot Unsupervised Domain Adaptation+1Domain Adaptation with a Single Vision-Language Embedding
Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in some uncommon conditions. In this paper, we pres…
Domain AdaptationOne-shot Unsupervised Domain AdaptationSemantic SegmentationUnsupervised Domain AdaptationLearnable Data Augmentation for One-Shot Unsupervised Domain Adaptation
This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem. OS-UDA is the most challenging setting in Domain Adaptation, as …
Data AugmentationDecoderDomain AdaptationOne-shot Unsupervised Domain Adaptation+2Target-driven One-Shot Unsupervised Domain Adaptation
In this paper, we introduce a novel framework for the challenging problem of One-Shot Unsupervised Domain Adaptation (OSUDA), which aims to adapt to a target domain with only a single unlabeled target sample. Unlike exis…
Domain AdaptationOne-shot Unsupervised Domain AdaptationUnsupervised Domain AdaptationOne-shot Unsupervised Domain Adaptation with Personalized Diffusion Models
Adapting a segmentation model from a labeled source domain to a target domain, where a single unlabeled datum is available, is one the most challenging problems in domain adaptation and is otherwise known as one-shot uns…
Data AugmentationDomain AdaptationOne-shot Unsupervised Domain AdaptationStyle Transfer+1PODA: Prompt-driven Zero-shot Domain Adaptation
Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some uncommon conditions. In this paper, we propose the task o…
Domain Adaptationimage-classificationImage ClassificationLanguage Modeling+8Semantic Self-adaptation: Enhancing Generalization with a Single Sample
The lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation. Previous studies relied on the assumption of a static model, i. e., once the training process is complete, model…
Domain AdaptationDomain GeneralizationOne-shot Unsupervised Domain AdaptationSegmentation+1Learning Instance-Specific Adaptation for Cross-Domain Segmentation
We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at test time, we adapt a pre-trained model by conducting instance-specific BatchNorm (statis…
Data AugmentationDomain AdaptationDomain GeneralizationImage Segmentation+4Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation
In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training. In this case, traditional unsupe…
Domain AdaptationOne-shot Unsupervised Domain AdaptationSemantic SegmentationStyle Transfer+1Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
We aim at the problem named One-Shot Unsupervised Domain Adaptation. Unlike traditional Unsupervised Domain Adaptation, it assumes that only one unlabeled target sample can be available when learning to adapt. This setti…
Domain Adaptationdomain classificationOne-shot Unsupervised Domain AdaptationStyle Transfer+1