Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning

• URL: http://arxiv.org/abs/2302.14794v1
• Date: Tue, 28 Feb 2023 17:46:18 GMT
• Title: Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning
• Authors: Ivona Najdenkoska, Xiantong Zhen, Marcel Worring
• Abstract summary: We introduce a multimodal meta-learning approach to bridge the gap between vision and language models. We define a meta-mapper network, acting as a meta-learner, to efficiently bridge frozen large-scale vision and language models. We evaluate our approach on recently proposed multimodal few-shot benchmarks, measuring how rapidly the model can bind novel visual concepts to words.
• Score: 38.37682598345653
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal few-shot learning is challenging due to the large domain gap between vision and language modalities. Existing methods are trying to communicate visual concepts as prompts to frozen language models, but rely on hand-engineered task induction to reduce the hypothesis space. To make the whole process learnable, we introduce a multimodal meta-learning approach. Specifically, our approach decomposes the training of the model into a set of related multimodal few-shot tasks. We define a meta-mapper network, acting as a meta-learner, to efficiently bridge frozen large-scale vision and language models and leverage their already learned capacity. By updating the learnable parameters only of the meta-mapper, it learns to accrue shared meta-knowledge among these tasks. Thus, it can rapidly adapt to newly presented samples with only a few gradient updates. Importantly, it induces the task in a completely data-driven manner, with no need for a hand-engineered task induction. We evaluate our approach on recently proposed multimodal few-shot benchmarks, measuring how rapidly the model can bind novel visual concepts to words and answer visual questions by observing only a limited set of labeled examples. The experimental results show that our meta-learning approach outperforms the baseline across multiple datasets and various training settings while being computationally more efficient.

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