CV-Probes: Studying the interplay of lexical and world knowledge in visually grounded verb understanding
This addresses a challenge in vision-language AI for researchers and developers, but it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of how vision-language transformer models ground verb phrases, finding that they struggle with context-dependent verbs like 'beg' compared to visually grounded ones like 'sit', with analysis showing insufficient attention to verb tokens.
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.