PCA of patch features. We show the results of the principal component analysis (PCA) performed on
the patch features extracted by our model. We keep only patches with a positive value after we threshold
the ﬁrst component. This procedure turns out to separate the image’s main object from the background. We
compute a second PCA on the remaining patches across three images depicting the same category. We color
the three ﬁrst components with three diﬀerent colors and present the results in Fig. 1 and 9. There are two
interesting observations: ﬁrst, our unsupervised foreground / background detector, based on detecting the

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DINOv2- Learning Robust Visual Features without Supervision