CV-Probes: Studying the interplay of lexical and world knowledge in visually grounded verb understanding

This study investigates the ability of various vision-language (VL) models to ground context-dependent and non-context-dependent verb phrases. To do that, we introduce the CV-Probes dataset, designed explicitly for studying context understanding, containing image-caption pairs with context-dependent...

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Bibliographic Details
Main Authors Beňová, Ivana, Gregor, Michal, Gatt, Albert
Format Journal Article
LanguageEnglish
Published 02.09.2024
Subjects
Computer Science - Computation and Language
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Summary:This study investigates the ability of various vision-language (VL) models to ground context-dependent and non-context-dependent verb phrases. To do that, we introduce the CV-Probes dataset, designed explicitly for studying context understanding, containing image-caption pairs with context-dependent verbs (e.g., "beg") and non-context-dependent verbs (e.g., "sit"). We employ the MM-SHAP evaluation to assess the contribution of verb tokens towards model predictions. Our results indicate that VL models struggle to ground context-dependent verb phrases effectively. These findings highlight the challenges in training VL models to integrate context accurately, suggesting a need for improved methodologies in VL model training and evaluation.
DOI:10.48550/arxiv.2409.01389