Audio-Visual Continual Test-Time Adaptation without Forgetting
arXiv:2602.18528v1 Announce Type: new Abstract: Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modalities can be distributionally shifted, which hampers online cross-modal learning and eventually leads to poor accuracy. While previous works have tackled this problem, we find that SOTA methods suffer from catastrophic forgetting, where the model's performance drops well below the source model due to continual parameter updates at test-time. In this work, we first show that adapting only the modality fusion layer to a target domain not only improves performance on that domain but can also enhance performance on subsequent domains. Based on this strong cross-task transferability of the fusion layer's parameters, we propose a method, $\texttt{AV-CTTA}$, that improves test-time performance of the models without access to any source data. Our approach works by using
arXiv:2602.18528v1 Announce Type: new Abstract: Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modalities can be distributionally shifted, which hampers online cross-modal learning and eventually leads to poor accuracy. While previous works have tackled this problem, we find that SOTA methods suffer from catastrophic forgetting, where the model's performance drops well below the source model due to continual parameter updates at test-time. In this work, we first show that adapting only the modality fusion layer to a target domain not only improves performance on that domain but can also enhance performance on subsequent domains. Based on this strong cross-task transferability of the fusion layer's parameters, we propose a method, $\texttt{AV-CTTA}$, that improves test-time performance of the models without access to any source data. Our approach works by using a selective parameter retrieval mechanism that dynamically retrieves the best fusion layer parameters from a buffer using only a small batch of test data. These parameters are then integrated into the model, adapted to the current test distribution, and saved back for future use. Extensive experiments on benchmark datasets involving unimodal and bimodal corruptions show our proposed $\texttt{AV-CTTA}$ significantly outperforms existing methods while minimizing catastrophic forgetting.
Executive Summary
This article presents a novel approach to audio-visual continual test-time adaptation, a critical challenge in cross-modal learning. The proposed method, $ exttt{AV-CTTA}$, leverages the strong cross-task transferability of the modality fusion layer's parameters to adapt to non-stationary domains without access to source data. By selectively retrieving and integrating the best fusion layer parameters, $ exttt{AV-CTTA}$ significantly outperforms existing methods while minimizing catastrophic forgetting. The approach is demonstrated on benchmark datasets involving unimodal and bimodal corruptions, showcasing its potential in real-world applications. The article contributes to the field of cross-modal learning by providing a practical solution to the test-time adaptation problem, which is crucial for developing robust and adaptive systems.
Key Points
- ▸ The article proposes a novel approach to audio-visual continual test-time adaptation, addressing the challenge of adapting to non-stationary domains without access to source data.
- ▸ The method, $ exttt{AV-CTTA}$, leverages the strong cross-task transferability of the modality fusion layer's parameters to improve test-time performance.
- ▸ The approach is demonstrated to significantly outperform existing methods while minimizing catastrophic forgetting on benchmark datasets.
Merits
Strength in Handling Non-Stationary Domains
The proposed method, $ exttt{AV-CTTA}$, effectively handles non-stationary domains without access to source data, which is a significant challenge in cross-modal learning.
Improved Test-Time Performance
The approach demonstrates significant improvement in test-time performance compared to existing methods, making it a valuable contribution to the field of cross-modal learning.
Minimized Catastrophic Forgetting
The method effectively minimizes catastrophic forgetting, a common issue in continual learning, which is essential for developing robust and adaptive systems.
Demerits
Limited Evaluation on Real-World Scenarios
The article primarily focuses on benchmark datasets, and it would be beneficial to evaluate the proposed method on real-world scenarios to further demonstrate its effectiveness.
Lack of Theoretical Analysis
A theoretical analysis of the proposed method would provide a deeper understanding of its underlying mechanisms and enhance its credibility.
Potential Overfitting to Benchmark Datasets
The approach relies on a buffer of fusion layer parameters, which may lead to overfitting to the benchmark datasets used for evaluation.
Expert Commentary
The article presents a novel and effective approach to audio-visual continual test-time adaptation, which is a critical challenge in cross-modal learning. The proposed method, $ exttt{AV-CTTA}$, leverages the strong cross-task transferability of the modality fusion layer's parameters to adapt to non-stationary domains without access to source data. The approach is demonstrated on benchmark datasets involving unimodal and bimodal corruptions, showcasing its potential in real-world applications. However, the article could be strengthened by providing a theoretical analysis of the proposed method and evaluating its performance on real-world scenarios. Nevertheless, the article contributes significantly to the field of cross-modal learning and has the potential to impact various applications and policy-making areas.
Recommendations
- ✓ Future research should focus on evaluating the proposed method on real-world scenarios to further demonstrate its effectiveness.
- ✓ A theoretical analysis of the proposed method would provide a deeper understanding of its underlying mechanisms and enhance its credibility.
- ✓ The approach could be extended to other domains, such as text or graph learning, to further showcase its versatility.
