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arXiv · 2305.15937

Visually grounded few-shot word acquisition with fewer shots

Leanne Nortje·Benjamin van Niekerk·Herman Kamper
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Abstract

We propose a visually grounded speech model that acquires new words and their visual depictions from just a few word-image example pairs. Given a set of test images and a spoken query, we ask the model which image depicts the query word. Previous work has simplified this problem by either using an artificial setting with digit word-image pairs or by using a large number of examples per class. We propose an approach that can work on natural word-image pairs but with less examples, i.e. fewer shots. Our approach involves using the given word-image example pairs to mine new unsupervised word-image training pairs from large collections of unlabelled speech and images. Additionally, we use a word-to-image attention mechanism to determine word-image similarity. With this new model, we achieve better performance with fewer shots than any existing approach.

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Leanne Nortje, Benjamin van Niekerk, Herman Kamper. 2023-05-25. Visually grounded few-shot word acquisition with fewer shots. https://arxiv.org/abs/2305.15937

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