The proposed metrics can be extended to evaluate the grounding capabilities of vision-language models in more complex, multi-object scenes by incorporating multi-instance grounding analysis. In such scenarios, where multiple objects are present in the scene, the metrics can be adapted to consider the relationships between different objects and their corresponding linguistic descriptions. This can involve analyzing the co-occurrence of activations for different objects mentioned in the prompt and comparing them to the ground-truth annotations for each object. By extending the metrics to handle multi-object scenes, the evaluation framework can provide a more comprehensive assessment of the model's ability to ground linguistic phrases to visual elements accurately in complex settings.