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Test-time Adaptation

Papers

Showing 101125 of 484 papers

TitleStatusHype
Distribution-Aware Continual Test-Time Adaptation for Semantic SegmentationCode1
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly LocalizationCode1
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical ImagesCode1
Bridging the Gap for Test-Time Multimodal Sentiment AnalysisCode1
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual LearningCode1
DomainAdaptor: A Novel Approach to Test-time AdaptationCode1
NOTE: Robust Continual Test-time Adaptation Against Temporal CorrelationCode1
Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature AlignmentCode1
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic SegmentationCode1
Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time AdaptationCode1
A Probabilistic Framework for Lifelong Test-Time AdaptationCode1
LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech RecognitionCode1
MECTA: Memory-Economic Continual Test-Time Model AdaptationCode1
MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical ReasoningCode1
AdaNPC: Exploring Non-Parametric Classifier for Test-Time AdaptationCode1
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time AdaptationCode1
Dual Prototype Evolving for Test-Time Generalization of Vision-Language ModelsCode1
Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time AdaptationCode1
Noisy Test-Time Adaptation in Vision-Language ModelsCode1
SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy MinimizationCode1
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image SegmentationCode1
SoTTA: Robust Test-Time Adaptation on Noisy Data StreamsCode1
EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled RegularizationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Harnessing Meta-Learning for Improving Full-Frame Video StabilizationCode1
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