Kernkonzepte
Efficient and effective test-time adaptation is achieved through a dynamic adapter, TDA, enhancing vision-language models' performance.
Zusammenfassung
The content discusses the development of a training-free dynamic adapter, TDA, for efficient and effective test-time adaptation of vision-language models. The article introduces the concept of TDA, highlighting its key features such as positive and negative cache models, progressive pseudo label refinement, and negative pseudo labeling. The TDA method is compared with existing state-of-the-art approaches, demonstrating superior effectiveness and efficiency in test-time adaptation. Extensive experiments over two benchmarks validate TDA's performance, showcasing significant improvements in accuracy and testing time reduction.
Directory:
Abstract
Introduction of TDA for efficient test-time adaptation.
Introduction
Overview of recent advances in vision-language models.
Test-Time Adaptation
Comparison of TDA with existing methods like TPT and DiffTPT.
Method
Detailed explanation of TDA's positive and negative cache models.
Experiments
Results of TDA's performance on OOD and cross-domain benchmarks.
Ablation Studies
Evaluation of Positive Cache, Negative Cache, and shot capacity.
Conclusion
Summary of TDA's contributions and effectiveness in test-time adaptation.
Statistiken
Extensive experiments over two benchmarks demonstrate TDA's superior effectiveness and efficiency.
TDA reduces testing time significantly from over 12 hours to 16 minutes on the ImageNet dataset.
Zitate
"TDA allows adapting to test data gradually via progressive pseudo label refinement."
"TDA is a dynamic cache that is training-free without any backpropagation, making it efficient for test-time adaptation."