Verb Categorisation

Verb categorization, a crucial aspect of natural language processing and computer vision, aims to understand how verbs function within sentences and images, focusing on their context-dependent meanings and relationships with other words and visual elements. Current research emphasizes improving the ability of models, particularly vision-language models (VLMs) and transformer architectures, to accurately classify and ground verbs, addressing challenges like ambiguity and context-dependency through techniques such as contrastive learning and explainable AI methods. These advancements have significant implications for various applications, including scene understanding, question answering, and solving mathematical word problems across different languages.