BAIR) (a) Input images (b) Co-segmented objects and parts (d) Correspondences Co-segmentation and part co-segmentation Point correspondence (c) Input image pair Figure 1: Deep ViT features applied to vision tasks. We demonstrate the effectiveness of deep features extracted from a selfsupervised, pre-trained ViT model (DINO-ViT) as dense patch descriptors via real-world vision tasks: (a-b) co-segmentation & part co-segmentation: given a set of input images (e.g., 4 input images), we automatically co-segment semantically common foreground objects (e.g., animals), and then further partition them into common parts; (c-d) point correspondence:given a pair of input images, we automatically extract a sparse set of corresponding points. We tackle these tasks by applying only lightweight, simple methodologies such as clustering or binning, to deep ViT features.