Context-Aware Multimodal Pretraining

• URL: http://arxiv.org/abs/2411.15099v1
• Date: Fri, 22 Nov 2024 17:55:39 GMT
• Title: Context-Aware Multimodal Pretraining
• Authors: Karsten Roth, Zeynep Akata, Dima Damen, Ivana Balažević, Olivier J. Hénaff,
• Abstract summary: We show that vision-language models can be trained to exhibit significantly increased few-shot adaptation. We find up to four-fold improvements in test-time sample efficiency, and average few-shot adaptation gains of over 5%.
• Score: 72.04020920042574
• License:
• Abstract: Large-scale multimodal representation learning successfully optimizes for zero-shot transfer at test time. Yet the standard pretraining paradigm (contrastive learning on large amounts of image-text data) does not explicitly encourage representations to support few-shot adaptation. In this work, we propose a simple, but carefully designed extension to multimodal pretraining which enables representations to accommodate additional context. Using this objective, we show that vision-language models can be trained to exhibit significantly increased few-shot adaptation: across 21 downstream tasks, we find up to four-fold improvements in test-time sample efficiency, and average few-shot adaptation gains of over 5%, while retaining zero-shot generalization performance across model scales and training durations. In particular, equipped with simple, training-free, metric-based adaptation mechanisms, our representations easily surpass more complex and expensive optimization-based schemes, vastly simplifying generalization to new domains.

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