Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details. To tackle these challenges, we represent images by "hyperpixels" that leverage a small number of relevant features selected among early to late layers of a convolutional neural network. Taking advantage of the condensed features of hyperpixels, we develop an effective real-time matching algorithm based on Hough geometric voting. The proposed method, hyperpixel flow, sets a new state of the art on three standard benchmarks as well as a new dataset, SPair-71k, which contains a significantly larger number of image pairs than existing datasets, with more accurate and richer annotations for in-depth analysis.
@article{arxiv.1908.06537,
title = {Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features},
author = {Juhong Min and Jongmin Lee and Jean Ponce and Minsu Cho},
journal= {arXiv preprint arXiv:1908.06537},
year = {2019}
}