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

• URL: http://arxiv.org/abs/2405.14977v2
• Date: Mon, 9 Sep 2024 17:33:57 GMT
• Title: A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models
• Authors: Mario Döbler, Robert A. Marsden, Tobias Raichle, Bin Yang,
• Abstract summary: In deep learning, maintaining robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models at test-time.
• Score: 3.0495235326282186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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