Insights gained from the proposed grounding evaluation framework can benefit various applications and tasks beyond phrase grounding and referring expression comprehension. One such application is visual question answering (VQA), where understanding the relationship between visual elements and textual queries is essential. By leveraging the insights on grounding uncertainty and activation localization provided by the metrics, VQA models can be improved to better align visual and textual information for accurate responses. Additionally, tasks like image-text matching, image captioning, and visual reasoning can benefit from a more nuanced evaluation of grounding capabilities to enhance the overall performance of vision-language models. The framework can also be applied to tasks in embodied AI, such as navigation-related challenges, where precise grounding of linguistic instructions to visual scenes is critical for successful task completion. Overall, the insights from the proposed evaluation framework can inform the development of more robust and effective vision-language models across a wide range of applications and tasks.