Fine-tuning models for specific tasks involves adjusting both visual datasets and the language tasks they perform. The grounding problem highlights the limitations of current models, particularly in understanding and describing objects not present in their training data, which can lead to significant inaccuracies. Recent research aims to address these challenges by incorporating novel object detection into language descriptions, improving the model's ability to caption previously unseen objects.
