  We introduce a few-shot localization dataset originating from photographers
who authentically were trying to learn about the visual content in the images
they took. It includes nearly 10,000 segmentations of 100 categories in over
4,500 images that were taken by people with visual impairments. Compared to
existing few-shot object detection and instance segmentation datasets, our
dataset is the first to locate holes in objects (e.g., found in 12.3\% of our
segmentations), it shows objects that occupy a much larger range of sizes
relative to the images, and text is over five times more common in our objects
(e.g., found in 22.4\% of our segmentations). Analysis of three modern few-shot
localization algorithms demonstrates that they generalize poorly to our new
dataset. The algorithms commonly struggle to locate objects with holes, very
small and very large objects, and objects lacking text. To encourage a larger
community to work on these unsolved challenges, we publicly share our annotated
few-shot dataset at https://vizwiz.org .
