What are the limitations of current Vision-Language Models in generating captions for complex scenes?

Current Vision-Language Models (VLMs) face several limitations when generating captions for complex scenes, primarily due to challenges in contextual understanding, handling abstract concepts, and maintaining consistency. While VLMs excel at recognizing common objects and basic relationships, they often struggle with scenes that involve nuanced interactions, layered semantics, or unconventional compositions. These limitations stem from their training data biases, architectural constraints, and difficulty interpreting implicit or symbolic information.

One major limitation is the inability to fully grasp contextual hierarchies and spatial relationships in crowded or dynamic scenes. For example, a VLM might correctly identify individual elements like “a dog,” “a Frisbee,” and “a park” in an image but fail to describe how they interact (e.g., “the dog leaps to catch the Frisbee mid-air”). Similarly, in a busy market scene, models might list objects but miss the narrative—like a vendor arguing with a customer while others watch. This happens because VLMs often prioritize object detection over inferring actions, emotions, or cause-effect relationships. They also struggle with relative positioning, such as distinguishing whether an object is “behind,” “under,” or “next to” another when perspectives are ambiguous.

Another issue is handling abstract or domain-specific content. VLMs trained on general datasets may misinterpret metaphors, cultural references, or technical visuals. For instance, a painting depicting “war as a storm” might be literally described as “dark clouds over a battlefield” without capturing the symbolism. Similarly, in medical imaging, a VLM might inaccurately caption an X-ray by using layman’s terms (“a white spot on bones”) instead of recognizing a fracture. These models also lack commonsense reasoning—like knowing that a person holding an umbrella in sunshine is unusual—leading to descriptions that miss contradictions or contextual absurdities.

Finally, VLMs often produce inconsistent or overly generic captions for complex scenes. They might generate plausible but incorrect details (e.g., describing a winter scene as “sunny” if snow isn’t prominent) or repeat safe phrases like “a group of people” instead of specifying actions. This stems from training objectives that prioritize broad accuracy over precision. For developers, addressing these gaps requires improving spatial reasoning modules, integrating domain-specific knowledge, and designing loss functions that penalize vagueness. Until then, VLMs will remain limited in scenarios demanding fine-grained, context-aware descriptions.