SPair-71k

Establishing visual correspondences under large intra-class variations, which is often referred to as semantic correspondence or semantic matching, remains a challenging problem in computer vision. Despite its significance, however, most of the datasets for semantic correspondence are limited to a small amount of image pairs with similar viewpoints and scales. In this paper, we present a new large-scale benchmark dataset of semantically paired images, SPair-71k, which contains 70,958 image pairs with diverse variations in viewpoint and scale. Compared to previous datasets, it is significantly larger in number and contains more accurate and richer annotations. We believe this dataset will provide a reliable testbed to study the problem of semantic correspondence and will help to advance research in this area. We provide the results of recent methods on our new dataset as baselines for further research. This huggingface version of the dataset is inofficial. It downloads the data from the original source and converts it to the huggingface format. ## Terms of Use The SPair-71k data includes images and metadata obtained from the [PASCAL-VOC](http://host.robots.ox.ac.uk/pascal/VOC/) and [flickr](https://www.flickr.com/) website. Use of these images and metadata must respect the corresponding [terms of use](https://www.flickr.com/help/terms).

Papers using SPair-71k (5)

Show, Match And Segment: Joint Weakly Supervised Learning Of Semantic Matching And Object Co-segmentation2019 · 61 citesEmergent Correspondence From Image Diffusion2023 · 54 citesTransformatcher: Match-to-match Attention For Semantic Correspondence2022 · 30 citesSd4match: Learning To Prompt Stable Diffusion Model For Semantic Matching2023 · 23 citesDo It Yourself: Learning Semantic Correspondence From Pseudo-labels2025 · 1 cites