Disentangling Pictorial Cue Understanding from Language Bias in VLMs via Depth Ordering Task
In this paper, we study depth perception of vision-language models (VLMs) to isolate the effects of pictorial depth cues and disentangle vision and language influences on model performance. To this end, we combine depth-ordering and odd-one-out psychophysical tasks: the VLMs are presented with images where one object is at different depth relative to other, otherwise identical, objects, and must determine whether the odd-one-out target is closer or farther to the observer. To create stimuli, we generate 2D views from simulated and real 3D scenes while controlling the presence of individual pictorial depth cues, enabling a fine-grained analysis of cue-level contributions. Language effects ...
| Authors | Yiqian Liu, Iuliia Kotseruba, John K. Tsotsos |
|---|---|
| Primary category | cs.CV |
| Published | 1 July 2026 |
| Last updated at source | 17 August 2026 |
Models and datasets referencing this paper
- liuyiqian/O3-DDataset
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