  Sketching is a powerful tool for creating abstract images that are sparse but
meaningful. Sketch understanding poses fundamental challenges for
general-purpose vision algorithms because it requires robustness to the
sparsity of sketches relative to natural visual inputs and because it demands
tolerance for semantic ambiguity, as sketches can reliably evoke multiple
meanings. While current vision algorithms have achieved high performance on a
variety of visual tasks, it remains unclear to what extent they understand
sketches in a human-like way. Here we introduce SEVA, a new benchmark dataset
containing approximately 90K human-generated sketches of 128 object concepts
produced under different time constraints, and thus systematically varying in
sparsity. We evaluated a suite of state-of-the-art vision algorithms on their
ability to correctly identify the target concept depicted in these sketches and
to generate responses that are strongly aligned with human response patterns on
the same sketch recognition task. We found that vision algorithms that better
predicted human sketch recognition performance also better approximated human
uncertainty about sketch meaning, but there remains a sizable gap between model
and human response patterns. To explore the potential of models that emulate
human visual abstraction in generative tasks, we conducted further evaluations
of a recently developed sketch generation algorithm (Vinker et al., 2022)
capable of generating sketches that vary in sparsity. We hope that public
release of this dataset and evaluation protocol will catalyze progress towards
algorithms with enhanced capacities for human-like visual abstraction.
