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arXiv · 2211.04971

Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions

Michele Cafagna·Kees van Deemter·Albert Gatt
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Abstract

Image captioning models tend to describe images in an object-centric way, emphasising visible objects. But image descriptions can also abstract away from objects and describe the type of scene depicted. In this paper, we explore the potential of a state-of-the-art Vision and Language model, VinVL, to caption images at the scene level using (1) a novel dataset which pairs images with both object-centric and scene descriptions. Through (2) an in-depth analysis of the effect of the fine-tuning, we show (3) that a small amount of curated data suffices to generate scene descriptions without losing the capability to identify object-level concepts in the scene; the model acquires a more holistic view of the image compared to when object-centric descriptions are generated. We discuss the parallels between these results and insights from computational and cognitive science research on scene perception.

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Michele Cafagna, Kees van Deemter, Albert Gatt. 2022-11-09. Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions. https://arxiv.org/abs/2211.04971

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