DOTA: Distributional Test-Time Adaptation of Vision-Language Models (2409.19375v1)

Abstract: Vision-language foundation models (e.g., CLIP) have shown remarkable performance across a wide range of tasks. However, deploying these models may be unreliable when significant distribution gaps exist between the training and test data. The training-free test-time dynamic adapter (TDA) is a promising approach to address this issue by storing representative test samples to guide the classification of subsequent ones. However, TDA only naively maintains a limited number of reference samples in the cache, leading to severe test-time catastrophic forgetting when the cache is updated by dropping samples. In this paper, we propose a simple yet effective method for DistributiOnal Test-time Adaptation (Dota). Instead of naively memorizing representative test samples, Dota continually estimates the distributions of test samples, allowing the model to continually adapt to the deployment environment. The test-time posterior probabilities are then computed using the estimated distributions based on Bayes' theorem for adaptation purposes. To further enhance the adaptability on the uncertain samples, we introduce a new human-in-the-loop paradigm which identifies uncertain samples, collects human-feedback, and incorporates it into the Dota framework. Extensive experiments validate that Dota enables CLIP to continually learn, resulting in a significant improvement compared to current state-of-the-art methods.

Overview of DOTA: Distributional Test-Time Adaptation of Vision-LLMs

The paper presents a novel method named Distributional Test-Time Adaptation (DOTA), designed to address the challenges faced by vision-language foundation models, particularly in handling distributional shifts between training and test datasets. While models like CLIP excel in zero-shot classification using large-scale image-text pairs, their performance can degrade with significant distributional variations at test time.

DOTA stands out for its approach to dynamically estimate the distribution of test samples rather than naively storing representative test samples, as seen in Training-Free Dynamic Adapters (TDA). The method leverages a Gaussian distribution assumption for embedding distributions and utilizes Bayes' theorem for posterior probability calculation, enabling approximately 20 times faster inference by eschewing gradient backpropagation.

Key Contributions and Methodology

1. Distributional Test-Time Adaptation: The paper proposes a continual learning framework that estimates the distribution of test samples online. The framework improves model performance in downstream tasks by efficiently calculating posterior probabilities based on Bayes' theorem.
2. Gaussian Discriminant Analysis: Utilizing Gaussian discriminant analysis, DOTA estimates the distribution of each class and adapts the model during testing. The approach avoids backpropagation and optimizes the efficiency of classifying new test samples.
3. Human-in-the-loop Paradigm: To enhance performance on uncertain samples, the paper introduces a human-in-the-loop approach. By identifying high-uncertainty samples, the model incorporates human feedback to refine its test-time adaptation process.
4. Adaptive Fusion Mechanism: DOTA integrates zero-shot and test-time classifiers, adapting its reliance on estimated distributions based on the number of available test samples.

Experimental Evaluation

The paper provides comprehensive experiments across domains featuring natural distribution shifts and cross-domain generalization scenarios. DOTA is validated against state-of-the-art methods such as TPT, DiffTPT, and TDA, showing superior performance and efficiency. In particular, DOTA exhibits an improvement of 0.99% average accuracy over the next best method using the ViT-B/16 backbone, confirming its capability in addressing distributional shifts without the computational overhead typical of gradient-based approaches.

Implications and Future Work

This research contributes practically by offering a method that enhances model adaptability and efficiency, which is crucial for real-world deployment. The proposed framework addresses significant challenges in adapting vision-LLMs to unseen data and presents possibilities for applications in dynamic environments where model performance must remain consistent.

The paper introduces the task of test-time adaptation with human feedback, opening new research directions in Human-AI collaboration. Future work could refine methods for selecting uncertain samples more accurately and improve the reliability of model updates based on human feedback.

Conclusion

DOTA presents a meaningful advancement in the field of test-time adaptation for vision-LLMs, emphasizing continuous learning from test data distributions. Its methodological contributions and experimental results demonstrate a promising direction for enhancing adaptability, reducing resource-intensive processes, and effectively incorporating human feedback in uncertain scenarios. As foundation models play increasingly pivotal roles across diverse fields, DOTA's advancements align well with the current needs for robust, adaptable AI systems.