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

TL;DR

This paper introduces the CV-Probes dataset to evaluate how vision-language models understand verb phrases requiring social and visual context, revealing current models' limitations in grounding context-dependent verbs.

Contribution

The paper presents a new dataset, CV-Probes, for assessing VL models' ability to interpret verb phrases with social and visual context, highlighting their current shortcomings.

Findings

VL models struggle with context-dependent verb grounding.

Models often neglect the verb token in captions.

Need for improved training and evaluation methods.

Abstract

How do vision-language (VL) transformer models ground verb phrases and do they integrate contextual and world knowledge in this process? We introduce the CV-Probes dataset, containing image-caption pairs involving verb phrases that require both social knowledge and visual context to interpret (e.g., "beg"), as well as pairs involving verb phrases that can be grounded based on information directly available in the image (e.g., "sit"). We show that VL models struggle to ground VPs that are strongly context-dependent. Further analysis using explainable AI techniques shows that such models may not pay sufficient attention to the verb token in the captions. Our results suggest a need for improved methodologies in VL model training and evaluation. The code and dataset will be available https://github.com/ivana-13/CV-Probes.