Papers Single-Source Domain Generalization
“Single-Source Domain Generalization” 태그가 달린 논문 57편 · 필터 해제
Generalizable Single-Source Cross-modality Medical Image Segmentation via Invariant Causal Mechanisms
Single-source domain generalization (SDG) aims to learn a model from a single source domain that can generalize well on unseen target domains. This is an important task in computer vision, particularly relevant to medica…
Domain GeneralizationImage SegmentationMedical Image SegmentationSemantic Segmentation+1Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalization
Open-set single-source domain generalization aims to use a single-source domain to learn a robust model that can be generalized to unknown target domains with both domain shifts and label shifts. The scarcity of the sour…
Domain Generalizationimage-classificationImage ClassificationSingle-Source Domain GeneralizationAligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification
Person Re-identification (Person ReID) has advanced significantly in fully supervised and domain generalized Person R e ID. However, methods developed for one task domain transfer poorly to the other. An ideal Person ReI…
Domain GeneralizationPerson Re-IdentificationSingle-Source Domain GeneralizationCrafting Distribution Shifts for Validation and Training in Single Source Domain Generalization
Single-source domain generalization attempts to learn a model on a source domain and deploy it to unseen target domains. Limiting access only to source domain data imposes two key challenges - how to train a model that c…
Domain GeneralizationImage to sketch recognitionPhoto to Rest GeneralizationSingle-Source Domain GeneralizationPrompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
Deep learning based methods often suffer from performance degradation caused by domain shift. In recent years, many sophisticated network structures have been designed to tackle this problem. However, the advent of large…
Domain GeneralizationImage SegmentationMedical Image SegmentationSegmentation+3Structure-Aware Single-Source Generalization with Pixel-Level Disentanglement for Joint Optic Disc and Cup Segmentation
Deploying deep segmentation models in new medical centers poses a significant challenge due to statistical disparities between source and unknown domains. Recent advancements in domain generalization (DG) have shown impr…
DisentanglementDomain GeneralizationMedical Image AnalysisMedical Image Segmentation+1Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis
The field of medical image segmentation is challenged by domain generalization (DG) due to domain shifts in clinical datasets. The DG challenge is exacerbated by the scarcity of medical data and privacy concerns. Traditi…
Data AugmentationDomain GeneralizationImage SegmentationMedical Image Segmentation+2Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis
Domain generalization achieves fault diagnosis on unseen modes. In process industrial systems, fault samples are limited, and only single-mode fault data can be obtained. Extracting domain-invariant fault features from s…
Contrastive LearningDiversityDomain GeneralizationFault Diagnosis+1FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation
Single-source domain generalization (SDG) in medical image segmentation (MIS) aims to generalize a model using data from only one source domain to segment data from an unseen target domain. Despite substantial advances i…
Data AugmentationDomain GeneralizationImage SegmentationMedical Image Segmentation+3WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization
Language has been useful in extending the vision encoder to data from diverse distributions without empirical discovery in training domains. However, as the image description is mostly at coarse-grained level and ignores…
Domain GeneralizationImage DescriptionSingle-Source Domain GeneralizationUnbiased Faster R-CNN for Single-source Domain Generalized Object Detection
Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm performance significantly. However, existing methods…
AttributeData AugmentationDomain GeneralizationObject+3RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation
Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced in clinical settings due to the scarcit…
Data AugmentationDomain GeneralizationImage SegmentationMedical Image Segmentation+3Language Guided Domain Generalized Medical Image Segmentation
Single source domain generalization (SDG) holds promise for more reliable and consistent image segmentation across real-world clinical settings particularly in the medical domain, where data privacy and acquisition cost …
Contrastive LearningDomain AdaptationDomain GeneralizationImage Segmentation+4MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation
The task of single-source domain generalization (SDG) in medical image segmentation is crucial due to frequent domain shifts in clinical image datasets. To address the challenge of poor generalization across different do…
Data AugmentationDomain GeneralizationImage ReconstructionImage Segmentation+3Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation
Domain Generalization (DG) aims to reduce domain shifts between domains to achieve promising performance on the unseen target domain, which has been widely practiced in medical image segmentation. Single-source domain ge…
Domain GeneralizationImage SegmentationMedical Image SegmentationSegmentation+2HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalization
Lane detection is a vital task for vehicles to navigate and localize their position on the road. To ensure reliable driving, lane detection models must have robust generalization performance in various road environments.…
DiversityDomain AdaptationDomain GeneralizationLane Detection+3Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen…
Domain GeneralizationImage SegmentationMedical Image SegmentationSegmentation+2Adversarial Bayesian Augmentation for Single-Source Domain Generalization
Generalizing to unseen image domains is a challenging problem primarily due to the lack of diverse training data, inaccessible target data, and the large domain shift that may exist in many real-world settings. As such d…
Data AugmentationDomain GeneralizationPhoto to Rest GeneralizationSingle-Source Domain GeneralizationCNN Feature Map Augmentation for Single-Source Domain Generalization
In search of robust and generalizable machine learning models, Domain Generalization (DG) has gained significant traction during the past few years. The goal in DG is to produce models which continue to perform well when…
Domain Generalizationimage-classificationImage ClassificationSingle-Source Domain GeneralizationFrequency Decomposition to Tap the Potential of Single Domain for Generalization
Domain generalization (DG), aiming at models able to work on multiple unseen domains, is a must-have characteristic of general artificial intelligence. DG based on single source domain training data is more challenging d…
Domain GeneralizationSingle-Source Domain Generalization