The Limits of Learning from Pictures and Text: Vision-Language Models and Embodied Scene Understanding

Researchers have conducted experiments comparing vision-language models (VLMs) to human observers in understanding scenes across various tasks, revealing a significant gap in VLM performance for affordance-based tasks despite high accuracy in general knowledge tasks. This highlights the limitations of learning from image-text pairs and suggests that embodied experience is crucial for certain aspects of visual cognition that cannot be captured through statistical co-occurrence alone.

Read the full article at arXiv cs.CV (Vision)

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