A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models

• URL: http://arxiv.org/abs/2312.12730v2
• Date: Mon, 25 Mar 2024 18:49:52 GMT
• Title: A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models
• Authors: Julio Silva-Rodríguez, Sina Hajimiri, Ismail Ben Ayed, Jose Dolz,
• Abstract summary: We show that state-of-the-artETL approaches exhibit strong performance only in narrowly-defined experimental setups. We propose a CLass-Adaptive linear Probe (CLAP) objective, whose balancing term is optimized via an adaptation of the general Augmented Lagrangian method.
• Score: 19.17722702457403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made, we reveal that state-of-the-art ETL approaches exhibit strong performance only in narrowly-defined experimental setups, and with a careful adjustment of hyperparameters based on a large corpus of labeled samples. In particular, we make two interesting, and surprising empirical observations. First, to outperform a simple Linear Probing baseline, these methods require to optimize their hyper-parameters on each target task. And second, they typically underperform -- sometimes dramatically -- standard zero-shot predictions in the presence of distributional drifts. Motivated by the unrealistic assumptions made in the existing literature, i.e., access to a large validation set and case-specific grid-search for optimal hyperparameters, we propose a novel approach that meets the requirements of real-world scenarios. More concretely, we introduce a CLass-Adaptive linear Probe (CLAP) objective, whose balancing term is optimized via an adaptation of the general Augmented Lagrangian method tailored to this context. We comprehensively evaluate CLAP on a broad span of datasets and scenarios, demonstrating that it consistently outperforms SoTA approaches, while yet being a much more efficient alternative.

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