Papers Semi-supervised Domain Adaptation
“Semi-supervised Domain Adaptation” 태그가 달린 논문 133편 · 필터 해제
COARSE: Collaborative Pseudo-Labeling with Coarse Real Labels for Off-Road Semantic Segmentation
Autonomous off-road navigation faces challenges due to diverse, unstructured environments, requiring robust perception with both geometric and semantic understanding. However, scarce densely labeled semantic data limits …
Domain AdaptationSemantic SegmentationSemi-supervised Domain AdaptationRoHan: Robust Hand Detection in Operation Room
Hand-specific localization has garnered significant interest within the computer vision community. Although there are numerous datasets with hand annotations from various angles and settings, domain transfer techniques f…
Data AugmentationDomain AdaptationHand DetectionSemi-supervised Domain AdaptationSource-free Semantic Regularization Learning for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) has been extensively researched due to its ability to improve classification performance and generalization ability of models by using a small amount of labeled data on the target…
Domain AdaptationSemi-supervised Domain AdaptationSemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation
Domain Adaptation (DA) and Semi-supervised Learning (SSL) converge in Semi-supervised Domain Adaptation (SSDA), where the objective is to transfer knowledge from a source domain to a target domain using a combination…
Domain AdaptationRepresentation LearningSegmentationSemantic Segmentation+1HiGDA: Hierarchical Graph of Nodes to Learn Local-to-Global Topology for Semi-Supervised Domain Adaptation
The enhanced representational power and broad applicability of deep learning models have attracted significant interest from the research community in recent years. However, these models often struggle to perform effecti…
Domain AdaptationSemi-supervised Domain AdaptationSemi-Supervised Transfer Boosting (SS-TrBoosting)
Semi-supervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. Previou…
Domain AdaptationSemi-supervised Domain AdaptationSource-Free Domain AdaptationUnsupervised Domain AdaptationKnowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification
Electroencephalogram (EEG)-based seizure subtype classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with n…
ClassificationDomain AdaptationEEGElectroencephalogram (EEG)+4The Last Mile to Supervised Performance: Semi-Supervised Domain Adaptation for Semantic Segmentation
Supervised deep learning requires massive labeled datasets, but obtaining annotations is not always easy or possible, especially for dense tasks like semantic segmentation. To overcome this issue, numerous works explore …
Contrastive LearningDomain AdaptationSemantic SegmentationSemi-supervised Domain Adaptation+1AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation
In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its generalization capability for the target…
Domain AdaptationGraph LearningSemi-supervised Domain AdaptationLearning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) has been widely studied due to its ability to utilize a few labeled target data to improve the generalization ability of the model. However, existing methods only consider designi…
Contrastive LearningDomain AdaptationSemi-supervised Domain AdaptationProgressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data
Recently, an intriguing research trend for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery has arisen: using simulated data to train ATR models is a feasible solution to the issue of inadeq…
Data AugmentationDomain AdaptationSemi-supervised Domain AdaptationUnsupervised Domain AdaptationGLA-DA: Global-Local Alignment Domain Adaptation for Multivariate Time Series
Unlike images and natural language tokens, time series data is highly semantically sparse, resulting in labor-intensive label annotations. Unsupervised and Semi-supervised Domain Adaptation (UDA and SSDA) have demonstrat…
Domain AdaptationSemi-supervised Domain AdaptationTime SeriesIs user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data
This paper aims to adapt the source model to the target environment, leveraging small user feedback (i.e., labeled target data) readily available in real-world applications. We find that existing semi-supervised domain a…
Domain Adaptationimage-classificationImage ClassificationSemantic Segmentation+1Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling
Semi-supervised domain adaptation methods leverage information from a source labelled domain with the goal of generalizing over a scarcely labelled target domain. While this setting already poses challenges due to potent…
DisentanglementDomain AdaptationSemi-supervised Domain AdaptationSemi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detection
3D object detection is crucial for applications like autonomous driving and robotics. However, in real-world environments, variations in sensor data distribution due to sensor upgrades, weather changes, and geographic di…
3D Object DetectionAutonomous DrivingDomain Adaptationobject-detection+2DALLMi: Domain Adaption for LLM-based Multi-label Classifier
Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movi…
Domain AdaptationLanguage ModelingLanguage ModellingLarge Language Model+2Semi-Supervised Domain Adaptation for Wildfire Detection
Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adap…
Domain Adaptationobject-detectionObject DetectionSemi-supervised Domain AdaptationEnhancing Semi-supervised Domain Adaptation via Effective Target Labeling
Existing semi-supervised domain adaptation (SSDA) models have exhibited impressive performance on the target domain by effectively utilizing few labeled target samples per class (e.g., 3 samples per class). To guarante…
Active LearningDomain AdaptationSemi-supervised Domain AdaptationUniversal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias
Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain. In this work, we introduce Universal Semi-Supervise…
Domain AdaptationPseudo LabelSemi-supervised Domain AdaptationUniversal Domain AdaptationAdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space
Semi-supervised domain adaptation (SSDA) presents a critical hurdle in computer vision, especially given the frequent scarcity of labeled data in real-world settings. This scarcity often causes foundation models, trained…
Domain AdaptationSemi-supervised Domain Adaptation