Evaluation of Multilingual Ability to Use Spatial Deictic Expressions in Vision-Language Models
arXiv:2607.07251v1 Announce Type: new Abstract: One of the expected abilities of vision-language models (VLMs) is spatial reasoning ability based on a given text and image. To evaluate the spatial reasoning abilities of VLMs, we focus on the use of spatial deictic expressions, which are defined as spatial expressions whose referent is determined by their situational context, such as ``this'' and ``that''. To handle spatial deictic expressions, VLMs must jointly reason over language and visual space, grounding context-dependent references in the image's spatial structure. In addition, selecting
The continuous development and evaluation of Vision-Language Models (VLMs) necessitate increasingly sophisticated benchmarks, and assessing spatial reasoning abilities is a current frontier.
Improving VLM's spatial reasoning with context-dependent expressions is crucial for their deployment in real-world scenarios requiring precise interaction and understanding of physical environments, impacting various AI applications.
This research provides a more granular method for evaluating a specific, complex aspect of VLM intelligence, highlighting a current limitation and a path for improvement.
- · AI researchers
- · developers of Vision-Language Models
- · robotics
- · augmented reality
- · developers of less sophisticated VLMs
VLMs will incorporate more robust spatial reasoning capabilities, improving their contextual understanding.
Enhanced VLMs will enable more natural and effective human-AI interaction in applications involving spatial instructions or descriptions.
This could accelerate the development of fully autonomous AI agents capable of complex physical world navigation and interaction.
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Read at arXiv cs.CL