Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities

TL;DR

This paper introduces COMFORT, a new evaluation protocol for assessing how well vision-language models understand and reason about spatial language and frames of reference, revealing significant limitations in current models.

Contribution

The paper presents COMFORT, a systematic evaluation method for spatial reasoning in VLMs, and demonstrates their shortcomings in robustness, flexibility, and cross-cultural understanding.

Findings

VLMs show some alignment with English spatial conventions.

Models lack robustness and consistency in spatial reasoning.

Cross-lingual tests reveal models favor English conventions.

Abstract

Spatial expressions in situated communication can be ambiguous, as their meanings vary depending on the frames of reference (FoR) adopted by speakers and listeners. While spatial language understanding and reasoning by vision-language models (VLMs) have gained increasing attention, potential ambiguities in these models are still under-explored. To address this issue, we present the COnsistent Multilingual Frame Of Reference Test (COMFORT), an evaluation protocol to systematically assess the spatial reasoning capabilities of VLMs. We evaluate nine state-of-the-art VLMs using COMFORT. Despite showing some alignment with English conventions in resolving ambiguities, our experiments reveal significant shortcomings of VLMs: notably, the models (1) exhibit poor robustness and consistency, (2) lack the flexibility to accommodate multiple FoRs, and (3) fail to adhere to language-specific or culture-specific conventions in cross-lingual tests, as English tends to dominate other languages. With a growing effort to align vision-language models with human cognitive intuitions, we call for more attention to the ambiguous nature and cross-cultural diversity of spatial reasoning.