2019/04/08 by Zixin Luo, Tianwei Shen, Luo, Zixin +13 · 10 citations
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Multimodal Machine Learning Applications #Robotics and Sensor-Based Localization #cs.CV
paper · pdf · doi:10.48550/arxiv.1904.04084
Accepted to CVPR 2019 (oral), supplementary materials included. (https://github.com/lzx551402/contextdesc)
arxiv created 2019/04/08 · arxiv updated 2019/04/09
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.