On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?

• URL: http://arxiv.org/abs/2405.02266v1
• Date: Fri, 3 May 2024 17:34:02 GMT
• Title: On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?
• Authors: Maxime Zanella, Ismail Ben Ayed,
• Abstract summary: We introduce a robust MeanShift for Test-time Augmentation (MTA) MTA surpasses prompt-based methods without requiring this intensive training procedure. We extensively benchmark our method on 15 datasets and demonstrate MTA's superiority and computational efficiency.
• Score: 13.803180972839213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of large vision-language models, notably CLIP, has catalyzed research into effective adaptation techniques, with a particular focus on soft prompt tuning. Conjointly, test-time augmentation, which utilizes multiple augmented views of a single image to enhance zero-shot generalization, is emerging as a significant area of interest. This has predominantly directed research efforts toward test-time prompt tuning. In contrast, we introduce a robust MeanShift for Test-time Augmentation (MTA), which surpasses prompt-based methods without requiring this intensive training procedure. This positions MTA as an ideal solution for both standalone and API-based applications. Additionally, our method does not rely on ad hoc rules (e.g., confidence threshold) used in some previous test-time augmentation techniques to filter the augmented views. Instead, MTA incorporates a quality assessment variable for each view directly into its optimization process, termed as the inlierness score. This score is jointly optimized with a density mode seeking process, leading to an efficient training- and hyperparameter-free approach. We extensively benchmark our method on 15 datasets and demonstrate MTA's superiority and computational efficiency. Deployed easily as plug-and-play module on top of zero-shot models and state-of-the-art few-shot methods, MTA shows systematic and consistent improvements.

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