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Towards flexible object-centric visual perception, we propose a one-shotinstance-aware object keypoint (OKP) extraction approach, AnyOKP, whichleverages the powerful representation ability of pretrained vision transformer(ViT), and can obtain keypoints on multiple object instances of arbitrarycategory after learning from a support image. An off-the-shelf petrained ViT isdirectly deployed for generalizable and transferable feature extraction, whichis followed by training-free feature enhancement. The best-prototype pairs(BPPs) are searched for in support and query images based on appearancesimilarity, to yield instance-unaware candidate keypoints.Then, the entiregraph with all candidate keypoints as vertices are divided to sub-graphsaccording to the feature distributions on the graph edges. Finally, eachsub-graph represents an object instance. AnyOKP is evaluated on real objectimages collected with the cameras of a robot arm, a mobile robot, and asurgical robot, which not only demonstrates the cross-category flexibility andinstance awareness, but also show remarkable robustness to domain shift andviewpoint change.

AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT