CVPR 2024 Papers

Test-time adaptation (TTA) has emerged as a viable solution to adapt pre-trained models to domain shifts using unlabeled test data. However TTA faces challenges of adaptation failures due to its reliance on blind adaptation to unknown test samples in dynamic scenarios. Traditional methods for out-of-distribution performance estimation are limited by unrealistic assumptions in the TTA context such as requiring labeled data or re-training models. To address this issue we propose AETTA a label-free accuracy estimation algorithm for TTA. We propose the prediction disagreement as the accuracy estimate calculated by comparing the target model prediction with dropout inferences. We then improve the prediction disagreement to extend the applicability of AETTA under adaptation failures. Our extensive evaluation with four baselines and six TTA methods demonstrates that AETTA shows an average of 19.8%p more accurate estimation compared with the baselines. We further demonstrate the effectiveness of accuracy estimation with a model recovery case study showcasing the practicality of our model recovery based on accuracy estimation. The source code is available at https://github.com/taeckyung/AETTA.

Continual test-time domain adaptation (CTTA) aims to adapt the source pre-trained model to a continually changing target domain without additional data acquisition or labeling costs. This issue necessitates an initial performance enhancement within the present domain without labels while concurrently averting an excessive bias toward the current domain. Such bias exacerbates catastrophic forgetting and diminishes the generalization ability to future domains. To tackle the problem this paper designs a versatile framework to capture high-quality supervision signals from three aspects: 1) The adaptive thresholds are employed to determine the reliability of pseudo-labels; 2) The knowledge from the source pre-trained model is utilized to adjust the unreliable one and 3) By evaluating past supervision signals we calculate a diversity score to ensure subsequent generalization. In this way we form a complete supervisory signal generation framework which can capture the current domain discriminative and reserve generalization in future domains. Finally to avoid catastrophic forgetting we design a weighted soft parameter alignment method to explore the knowledge from the source model. Extensive experimental results demonstrate that our method performs well on several benchmark datasets.