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We present a method for joint alignment of sparse in-the-wild imagecollections of an object category. Most prior works assume either ground-truthkeypoint annotations or a large dataset of images of a single object category.However, neither of the above assumptions hold true for the long-tail of theobjects present in the world. We present a self-supervised technique thatdirectly optimizes on a sparse collection of images of a particularobject/object category to obtain consistent dense correspondences across thecollection. We use pairwise nearest neighbors obtained from deep features of apre-trained vision transformer (ViT) model as noisy and sparse keypoint matchesand make them dense and accurate matches by optimizing a neural network thatjointly maps the image collection into a learned canonical grid. Experiments onCUB and SPair-71k benchmarks demonstrate that our method can produce globallyconsistent and higher quality correspondences across the image collection whencompared to existing self-supervised methods. Code and other material will bemade available at \url{https://kampta.github.io/asic}.

ASIC: Aligning Sparse in-the-wild Image Collections