ContextDesc: Local Descriptor Augmentation With Cross-Modality Context
Zixin Luo, Tianwei Shen, Lei Zhou, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, Long Quan
Abstract
Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations. In this paper, we go beyond the local detail representation by introducing context awareness to augment off-the-shelf local feature descriptors. Specifically, we propose a unified learning framework that leverages and aggregates the cross-modality contextual information, including (i) visual context from high-level image representation, and (ii) geometric context from 2D keypoint distribution. Moreover, we propose an effective N-pair loss that eschews the empirical hyper-parameter search and improves the convergence. The proposed augmentation scheme is lightweight compared with the raw local feature description, meanwhile improves remarkably on several large-scale benchmarks with diversified scenes, which demonstrates both strong practicality and generalization ability in geometric matching applications.
BibTeX
@inproceedings{cvpr2019_contextdesclocal,
title = {ContextDesc: Local Descriptor Augmentation With Cross-Modality Context},
author = {Zixin Luo and Tianwei Shen and Lei Zhou and Jiahui Zhang and Yao Yao and Shiwei Li and Tian Fang and Long Quan},
booktitle = {CVPR 2019},
year = {2019}
}