A probabilistic Gaussian model with adaptive contrastive asymmetry rectification improves multi-modal test-time adaptation by modeling category distributions and correcting modality asymmetry for better predictions under shifts.
Dota: Distributional test-time adaptation of vision-language models
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ADAPT reframes test-time adaptation as probabilistic Gaussian inference with CLIP-guided regularization, delivering SOTA results without gradients, source data, or full target access in both online and transductive settings.
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Multi-modal Test-time Adaptation via Adaptive Probabilistic Gaussian Calibration
A probabilistic Gaussian model with adaptive contrastive asymmetry rectification improves multi-modal test-time adaptation by modeling category distributions and correcting modality asymmetry for better predictions under shifts.
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Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment
ADAPT reframes test-time adaptation as probabilistic Gaussian inference with CLIP-guided regularization, delivering SOTA results without gradients, source data, or full target access in both online and transductive settings.
- GRPO-TTA: Test-Time Visual Tuning for Vision-Language Models via GRPO-Driven Reinforcement Learning