Patch matching. Finally, we explore what type of information our patch-level features contain by match-
ing them across images. We start by detecting the foreground object using the procedure described above.
Then, we compute the euclidean distance between patch features extracted from two images and map them
by solving an assignment problem. In order to reduce the number of matches, we then apply a non-maximum
suppression to keep only the salient ones. In Fig. 10, we show some examples of such matchings.
We observe that the features seem to capture information about semantic regions that serve similar purpose
in diﬀerent objects or animals. For instance, the wing of a plane matches the wing of a bird. We also observe
that the model is robust to style (image versus drawing), and to large variation of poses (see the elephant).

8 Fairness and Bias Analysis

8.1 Geographical Fairness
DINOv2- Learning Robust Visual Features without Supervision