Papers Multi-target Domain Adaptation
“Multi-target Domain Adaptation” 태그가 달린 논문 39편 · 필터 해제
Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation
Model merging has emerged as a powerful technique for combining task-specific weights, achieving superior performance in multi-target domain adaptation. However, when applied to practical scenarios, such as quantized mod…
Domain AdaptationMulti-target Domain AdaptationQuantizationGrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains
Retinal vessel segmentation is critical for diagnosing ocular conditions, yet current deep learning methods are limited by modality-specific challenges and significant distribution shifts across imaging devices, resoluti…
Decision MakingDomain AdaptationMulti-target Domain AdaptationRetinal Vessel Segmentation+1COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation
Multi-Target Domain Adaptation (MTDA) entails learning domain-invariant information from a single source domain and applying it to multiple unlabeled target domains. Yet, existing MTDA methods predominantly focus on addr…
Domain AdaptationMulti-target Domain AdaptationOpen-Set Multi-Target Domain AdaptationPrompt LearningTraining-Free Model Merging for Multi-target Domain Adaptation
In this paper, we study multi-target domain adaptation of scene understanding models. While previous methods achieved commendable results through inter-domain consistency losses, they often assumed unrealistic simultaneo…
Domain AdaptationMulti-target Domain AdaptationScene UnderstandingOurDB: Ouroboric Domain Bridging for Multi-Target Domain Adaptive Semantic Segmentation
Multi-target domain adaptation (MTDA) for semantic segmentation poses a significant challenge, as it involves multiple target domains with varying distributions. The goal of MTDA is to minimize the domain discrepancies a…
Domain AdaptationMulti-target Domain AdaptationSemantic SegmentationConvLoRA and AdaBN based Domain Adaptation via Self-Training
Existing domain adaptation (DA) methods often involve pre-training on the source domain and fine-tuning on the target domain. For multi-target domain adaptation, having a dedicated/separate fine-tuned network for each ta…
Domain AdaptationMulti-target Domain AdaptationSemantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention
Semantic segmentation's performance is often compromised when applied to unlabeled adverse weather conditions. Unsupervised domain adaptation is a potential approach to enhancing the model's adaptability and robustness t…
Domain AdaptationMulti-target Domain AdaptationSemantic SegmentationUnsupervised Domain AdaptationMixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
We consider the problem of learning a model from multiple heterogeneous sources with the goal of performing well on a new target distribution. The goal of learner is to mix these data sources in a target-distribution awa…
Domain AdaptationMulti-target Domain AdaptationStrong-Weak Integrated Semi-supervision for Unsupervised Single and Multi Target Domain Adaptation
Unsupervised domain adaptation (UDA) focuses on transferring knowledge learned in the labeled source domain to the unlabeled target domain. Despite significant progress that has been achieved in single-target domain adap…
DiversityDomain Adaptationimage-classificationImage Classification+2MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds
Unsupervised domain adaptation (UDA) addresses the problem of distribution shift between the unlabelled target domain and labelled source domain. While the single target domain adaptation (STDA) is well studied in the li…
Autonomous DrivingDomain AdaptationMulti-target Domain AdaptationUnsupervised Domain AdaptationClass Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets, which yields limited performance, especi…
Blended-target Domain AdaptationDomain AdaptationLabel shift of blended-target domain adaptationMulti-target Domain AdaptationOpen-Set Multi-Source Multi-Target Domain Adaptation
Single-Source Single-Target Domain Adaptation (1S1T) aims to bridge the gap between a labelled source domain and an unlabelled target domain. Despite 1S1T being a well-researched topic, they are typically not deployed to…
Domain AdaptationGraph AttentionMulti-target Domain AdaptationCyclically Disentangled Feature Translation for Face Anti-spoofing
Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary. However, it is usually difficult for th…
DisentanglementDomain AdaptationFace Anti-SpoofingMulti-target Domain Adaptation+1CoNMix for Source-free Single and Multi-target Domain Adaptation
This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of \textbf{Co}nsistency with \textbf{N}uclear-Norm Maximization and \textbf{Mix}Up knowledge …
Domain AdaptationKnowledge DistillationMulti-target Domain AdaptationPseudo LabelCooperative Self-Training for Multi-Target Adaptive Semantic Segmentation
In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in th…
Domain AdaptationMulti-target Domain AdaptationSegmentationSemantic SegmentationA Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training
Object detection algorithms allow to enable many interesting applications which can be implemented in different devices, such as smartphones and wearable devices. In the context of a cultural site, implementing these alg…
Domain AdaptationMulti-target Domain AdaptationObjectobject-detection+2Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trai…
3D Object DetectionAutonomous DrivingDomain AdaptationMulti-target Domain Adaptation+3PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the domain-shift which exists under the UDA set…
Action RecognitionDomain AdaptationDomain GeneralizationMulti-target Domain Adaptation+2Multi-Scale Multi-Target Domain Adaptation for Angle Closure Classification
Deep learning (DL) has made significant progress in angle closure classification with anterior segment optical coherence tomography (AS-OCT) images. These AS-OCT images are often acquired by different imaging devices/con…
Domain AdaptationMulti-target Domain AdaptationKnowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification
Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications. Several unsupervised single-target domain adaptation (STDA) methods have…
Domain AdaptationKnowledge DistillationMulti-target Domain AdaptationPerson Re-Identification