Seeing Through Their Eyes: Evaluating Visual Perspective Taking in Vision Language Models
This work addresses the need to assess nuanced cognitive abilities in VLMs for real-world deployment, though it is incremental as it focuses on evaluation rather than model improvement.
The paper tackled the problem of evaluating visual perspective-taking (VPT) in Vision Language Models (VLMs) by introducing two manually curated datasets, Isle-Bricks and Isle-Dots, and testing 12 VLMs, finding a significant performance drop when perspective-taking is required.
Visual perspective-taking (VPT), the ability to understand the viewpoint of another person, enables individuals to anticipate the actions of other people. For instance, a driver can avoid accidents by assessing what pedestrians see. Humans typically develop this skill in early childhood, but it remains unclear whether the recently emerging Vision Language Models (VLMs) possess such capability. Furthermore, as these models are increasingly deployed in the real world, understanding how they perform nuanced tasks like VPT becomes essential. In this paper, we introduce two manually curated datasets, Isle-Bricks and Isle-Dots for testing VPT skills, and we use it to evaluate 12 commonly used VLMs. Across all models, we observe a significant performance drop when perspective-taking is required. Additionally, we find performance in object detection tasks is poorly correlated with performance on VPT tasks, suggesting that the existing benchmarks might not be sufficient to understand this problem. The code and the dataset will be available at https://sites.google.com/view/perspective-taking