  Recent advances in zero-shot image recognition suggest that vision-language
models learn generic visual representations with a high degree of semantic
information that may be arbitrarily probed with natural language phrases.
Understanding an image, however, is not just about understanding what content
resides within an image, but importantly, where that content resides. In this
work we examine how well vision-language models are able to understand where
objects reside within an image and group together visually related parts of the
imagery. We demonstrate how contemporary vision and language representation
learning models based on contrastive losses and large web-based data capture
limited object localization information. We propose a minimal set of
modifications that results in models that uniquely learn both semantic and
spatial information. We measure this performance in terms of zero-shot image
recognition, unsupervised bottom-up and top-down semantic segmentations, as
well as robustness analyses. We find that the resulting model achieves
state-of-the-art results in terms of unsupervised segmentation, and demonstrate
that the learned representations are uniquely robust to spurious correlations
in datasets designed to probe the causal behavior of vision models.
