SPair-71k: A Large-scale Benchmark for Semantic Correspondence
This provides a reliable testbed for researchers in computer vision to study semantic correspondence, though it is incremental as it builds on existing dataset efforts.
The authors tackled the problem of semantic correspondence in computer vision by introducing SPair-71k, a large-scale benchmark dataset containing 70,958 image pairs with diverse variations in viewpoint and scale, which is significantly larger and more richly annotated than previous datasets.
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. Our benchmark is available online at http://cvlab.postech.ac.kr/research/SPair-71k/.