Multi-Modal Fusion by Meta-Initialization

• URL: http://arxiv.org/abs/2210.04843v1
• Date: Mon, 10 Oct 2022 17:00:58 GMT
• Title: Multi-Modal Fusion by Meta-Initialization
• Authors: Matthew T. Jackson, Shreshth A. Malik, Michael T. Matthews, Yousuf Mohamed-Ahmed
• Abstract summary: We propose an extension to the Model-Agnostic Meta-Learning algorithm (MAML) This allows the model to adapt using auxiliary information as well as task experience. FuMI significantly outperforms uni-modal baselines such as MAML in the few-shot regime.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When experience is scarce, models may have insufficient information to adapt to a new task. In this case, auxiliary information - such as a textual description of the task - can enable improved task inference and adaptation. In this work, we propose an extension to the Model-Agnostic Meta-Learning algorithm (MAML), which allows the model to adapt using auxiliary information as well as task experience. Our method, Fusion by Meta-Initialization (FuMI), conditions the model initialization on auxiliary information using a hypernetwork, rather than learning a single, task-agnostic initialization. Furthermore, motivated by the shortcomings of existing multi-modal few-shot learning benchmarks, we constructed iNat-Anim - a large-scale image classification dataset with succinct and visually pertinent textual class descriptions. On iNat-Anim, FuMI significantly outperforms uni-modal baselines such as MAML in the few-shot regime. The code for this project and a dataset exploration tool for iNat-Anim are publicly available at https://github.com/s-a-malik/multi-few .

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