Current Vision-Language Models (VLMs) face several limitations when generating captions for complex scenes. One major challenge is the difficulty in accurately understanding spatial relationships and interactions among multiple objects. For example, in a scene depicting a busy street with people walking, cars parked, and a dog chasing a ball, a VLM might struggle to recognize which object is interacting with which. This could lead to generic or ambiguous captions, such as "There are many things happening," rather than a more precise description that conveys the scene's dynamics.
Another limitation is the models' tendency to focus on predominant objects while overlooking subtler details that contribute to the scene’s meaning. For instance, if an image captures a picnic with various food items, drinks, and people, a VLM might primarily mention the main elements like "people" and "food," but fail to highlight context-specific details such as "the red-checkered blanket" or "the lemonade pitcher." These details can significantly enrich a caption and enhance users’ understanding of the scene, yet the model often misses them due to a lack of fine-grained contextual awareness.
Lastly, VLMs may struggle with generating captions that incorporate cultural context or nuanced emotions within a scene. For example, a picture showing a celebration can vary widely in emotional tone depending on the cultural backdrop—what feels festive in one context might be interpreted differently in another. If a model generates a caption like "People are happy," it may not capture the underlying cultural significance or the specific emotions depicted in the image. This gap highlights the need for VLMs to possess deeper cultural knowledge and emotional intelligence to create truly insightful and accurate captions for complex scenes.