  Humans effortlessly grasp the connection between sketches and real-world
objects, even when these sketches are far from realistic. Moreover, human
sketch understanding goes beyond categorization -- critically, it also entails
understanding how individual elements within a sketch correspond to parts of
the physical world it represents. What are the computational ingredients needed
to support this ability? Towards answering this question, we make two
contributions: first, we introduce a new sketch-photo correspondence benchmark,
$\textit{PSC6k}$, containing 150K annotations of 6250 sketch-photo pairs across
125 object categories, augmenting the existing Sketchy dataset with
fine-grained correspondence metadata. Second, we propose a self-supervised
method for learning dense correspondences between sketch-photo pairs, building
upon recent advances in correspondence learning for pairs of photos. Our model
uses a spatial transformer network to estimate the warp flow between latent
representations of a sketch and photo extracted by a contrastive learning-based
ConvNet backbone. We found that this approach outperformed several strong
baselines and produced predictions that were quantitatively consistent with
other warp-based methods. However, our benchmark also revealed systematic
differences between predictions of the suite of models we tested and those of
humans. Taken together, our work suggests a promising path towards developing
artificial systems that achieve more human-like understanding of visual images
at different levels of abstraction. Project page:
https://photo-sketch-correspondence.github.io
