ASIC: Aligning Sparse in-the-wild Image Collections

• URL: http://arxiv.org/abs/2303.16201v1
• Date: Tue, 28 Mar 2023 17:59:28 GMT
• Title: ASIC: Aligning Sparse in-the-wild Image Collections
• Authors: Kamal Gupta, Varun Jampani, Carlos Esteves, Abhinav Shrivastava, Ameesh Makadia, Noah Snavely, Abhishek Kar
• Abstract summary: We present a method for joint alignment of sparse in-the-wild image collections of an object category. We use pairwise nearest neighbors obtained from deep features of a pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches. Experiments on CUB and SPair-71k benchmarks demonstrate that our method can produce globally consistent and higher quality correspondences.
• Score: 86.66498558225625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method for joint alignment of sparse in-the-wild image collections of an object category. Most prior works assume either ground-truth keypoint 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 the objects present in the world. We present a self-supervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection. We use pairwise nearest neighbors obtained from deep features of a pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches and make them dense and accurate matches by optimizing a neural network that jointly maps the image collection into a learned canonical grid. Experiments on CUB and SPair-71k benchmarks demonstrate that our method can produce globally consistent and higher quality correspondences across the image collection when compared to existing self-supervised methods. Code and other material will be made available at \url{https://kampta.github.io/asic}.

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