  In this work, we tackle one-shot visual search of object parts. Given a
single reference image of an object with annotated affordance regions, we
segment semantically corresponding parts within a target scene. We propose
AffCorrs, an unsupervised model that combines the properties of pre-trained
DINO-ViT's image descriptors and cyclic correspondences. We use AffCorrs to
find corresponding affordances both for intra- and inter-class one-shot part
segmentation. This task is more difficult than supervised alternatives, but
enables future work such as learning affordances via imitation and assisted
teleoperation.
