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A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models (2405.14977v2)

Published 23 May 2024 in cs.CV

Abstract: In deep learning, maintaining model robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models at test-time, with a particular emphasis on CLIP and its variants. The study systematically examines prompt-based techniques and existing test-time adaptation methods, aiming to improve the robustness under distribution shift in diverse real-world scenarios. Specifically, the investigation covers various prompt engineering strategies, including handcrafted prompts, prompt ensembles, and prompt learning techniques. Additionally, we introduce a vision-text-space ensemble that substantially enhances average performance compared to text-space-only ensembles. Since online test-time adaptation has shown to be effective to mitigate performance drops under distribution shift, the study extends its scope to evaluate the effectiveness of existing test-time adaptation methods that were originally designed for vision-only classification models. Through extensive experimental evaluations conducted across multiple datasets and diverse model architectures, the research demonstrates the effectiveness of these adaptation strategies. Code is available at: https://github.com/mariodoebler/test-time-adaptation

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Authors (4)
  1. Mario Döbler (10 papers)
  2. Robert A. Marsden (8 papers)
  3. Tobias Raichle (1 paper)
  4. Bin Yang (320 papers)
Citations (4)
View on Semantic Scholar

Summary

Online Test-Time Adaptation for Vision-LLMs: Enhancing Robustness Against Distribution Shifts

The paper "A Lost Opportunity for Vision-LLMs: A Comparative Study of Online Test-Time Adaptation for Vision-LLMs" by Döbler et al. provides an extensive examination of test-time adaptation (TTA) strategies applied to vision-language (VL) models under distribution shifts. At the heart of the paper is an evaluation of diverse methodologies aimed at maintaining and improving the robustness of VL models, specifically focusing on CLIP and its variants. The work explores the intricate details of prompt engineering and augments this exploration with an analysis of existing TTA methods originally designed for vision-only models.

Prompt-Based Techniques and Vision-Text-Space Ensemble

The paper presents an assessment of different prompt-based strategies, including handcrafted prompts, prompt ensembles, and learning prompts. Notably, it introduces a novel approach named the vision-text-space ensemble (VTE). The VTE enhances performance by leveraging test-time augmentation with entropy-based filtering to construct ensembles across both the vision and text embedding spaces without additional optimization effort during inference. This approach not only reduces reliance on single prompts but demonstrates notable improvements, outperforming standard prompt engineering methodologies.

Evaluation and Impact of Existing TTA Methods

In extending the scope to TTA methods, the researchers systematically test these approaches on VL models, highlighting their potential to improve model robustness against distribution shifts. Methods such as TENT, ETA, SAR, and ROID are reevaluated within the context of VL models. The paper distinguishes itself by demonstrating that while some techniques did not yield substantial improvements in vision-language settings, others such as ROID and CMF showed measurable gains, even outperforming conventional prompt-tuned models in some cases. These findings underscore the continuing relevance and adaptability of traditional TTA methods when properly aligned with the multimodal frameworks inherent in vision-LLMs.

Numerical Results and Implications

Numerically, the paper compares the average error rates across numerous datasets and scenarios, revealing that effective adaptation strategies can significantly enhance the performance of models like CLIP. For example, the paper showed an absolute reduction in error rates by up to several percentage points across a variety of challenging datasets and task variations. These results underline the nuanced advantages that TTA can bring to VL models, demonstrating their potential to reduce error rates even in highly tuned architectures.

Practical and Theoretical Implications

From a practical standpoint, this research opens avenues for more robust application of VL models in dynamic real-world settings where data distribution shifts are prevalent and inevitable. The theoretical implications are equally significant, suggesting that foundation models like CLIP, when equipped with TTA strategies, can maintain their formidable zero-shot performance even under less controlled and unforeseen testing conditions.

Future Directions and Developments

While the paper provides a robust exploration of various adaptation strategies, it concurrently suggests several avenues for future research. Potential investigations could focus on fine-tuning the TTA strategies to minimize computational overhead, further integrating advanced augmentation techniques, and exploring adaptation performance across an even broader array of VL models and downstream tasks. Moreover, with the increasing application of VL models across industries, evolving TTA strategies to handle complex, multimodal domain shifts more effectively could be an area of active research.

In conclusion, Döbler et al.'s work provides valuable insights into enhancing vision-LLMs' robustness through test-time adaptation. It highlights the significant potential for current adaptation methodologies to traverse the challenges posed by distribution shifts, thereby bolstering the applicability and accuracy of foundation models in real-world scenarios.

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GitHub

  1. GitHub - mariodoebler/test-time-adaptation: A repository and benchmark for online test-time adaptation. (146 stars)