What Vision-Language Models `See' when they See Scenes
This work addresses the problem of evaluating alignment capabilities in vision-language models for researchers, highlighting CLIP's robustness but noting incremental insights.
The study investigated how well pretrained vision-language models align object- and scene-level descriptions with images, finding that CLIP consistently performs well on both, while other models show stylistic biases.
Images can be described in terms of the objects they contain, or in terms of the types of scene or place that they instantiate. In this paper we address to what extent pretrained Vision and Language models can learn to align descriptions of both types with images. We compare 3 state-of-the-art models, VisualBERT, LXMERT and CLIP. We find that (i) V&L models are susceptible to stylistic biases acquired during pretraining; (ii) only CLIP performs consistently well on both object- and scene-level descriptions. A follow-up ablation study shows that CLIP uses object-level information in the visual modality to align with scene-level textual descriptions.