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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0199796" target="_blank" >RIV/00216305:26230/26:0199796 - isvavai.cz</a>

Výsledek na webu

<a href="https://escholarship.org/content/qt3h83566r/qt3h83566r.pdf?v=lg" target="_blank" >https://escholarship.org/content/qt3h83566r/qt3h83566r.pdf?v=lg</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů