The grounding performance differences between the models can be attributed to their underlying architectural choices and training strategies. Models like ALBEFAMC, which showed superior grounding performance in the evaluation, may have architectural components that facilitate better alignment between visual and textual modalities. For instance, the use of attention mechanisms or fine-tuning strategies that emphasize consistent gradient-based explanations can enhance the model's grounding capabilities. On the other hand, models with larger parameter sizes, such as BLIPlarge, may struggle with grounding accuracy due to increased complexity and potential overfitting. Training strategies that focus on minimizing spurious activations outside the ground-truth bounding box, as seen in CLIPgScoreCAM, can also impact grounding performance positively. Therefore, the architectural design, parameter size, and training objectives play a crucial role in determining the grounding performance of vision-language models.