Small Visual Language Models can also be Open-Ended Few-Shot Learners

Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Cees G. M. Snoek, Marcel Worring, Yuki M Asano

Keywords: multimodal few-shot learning, visual language models, self-supervised learning, in-context learning

TL;DR: We present Self-Context Adaptation (SeCAt), a self-supervised approach for equipping small visual language models with in-context learning abilities, to perform open-ended few-shot learning.

Abstract: We present Self-Context Adaptation (SeCAt), a self-supervised approach that unlocks open-ended few-shot abilities of small visual language models. Our proposed adaptation algorithm explicitly learns from symbolic, yet self-supervised training tasks. Specifically, our approach imitates image captions in a self-supervised way based on clustering a large pool of images followed by assigning semantically-unrelated names to clusters. By doing so, we construct the `self-context', a training signal consisting of interleaved sequences of image and pseudo-caption pairs and a query image for which the model is trained to produce the right pseudo-caption. We demonstrate the performance and flexibility of SeCAt on several multimodal few-shot datasets, spanning various granularities. By using models with approximately 1B parameters we outperform the few-shot abilities of much larger models, such as Frozen and FROMAGe. SeCAt opens new possibilities for research in open-ended few-shot learning that otherwise requires access to large or proprietary models.

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Submission Number: 6949