  In this paper, we consider the task of one-shot object detection, which
consists in detecting objects defined by a single demonstration. Differently
from the standard object detection, the classes of objects used for training
and testing do not overlap. We build the one-stage system that performs
localization and recognition jointly. We use dense correlation matching of
learned local features to find correspondences, a feed-forward geometric
transformation model to align features and bilinear resampling of the
correlation tensor to compute the detection score of the aligned features. All
the components are differentiable, which allows end-to-end training.
Experimental evaluation on several challenging domains (retail products, 3D
objects, buildings and logos) shows that our method can detect unseen classes
(e.g., toothpaste when trained on groceries) and outperforms several baselines
by a significant margin. Our code is available online:
https://github.com/aosokin/os2d .
