Multivariate Regression on the Grassmannian for Predicting Novel Domains

Yongxin Yang, Timothy Hospedales

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We study the problem of predicting how to recognise visual objects in novel domains with neither labelled nor unlabelled training data. Domain adaptation is now an established research area due to its value in ameliorating the issue of domain shift between train and test data. However, it is conventionally assumed that domains are discrete entities, and that at least unlabelled data is provided in testing domains. In this paper, we consider the case where domains are parametrised by a vector of continuous values (e.g., time, lighting or view angle). We aim to use such domain metadata to predict novel domains for recognition. This allows a recognition model to be pre-calibrated for a new domain in advance (e.g., future time or view angle) without waiting for data collection and re-training. We achieve this by posing the problem as one of multivariate regression on the Grassmannian, where we regress a domain’s subspace (point on the Grassmannian) against an independent vector of domain parameters. We derive two novel methodologies to achieve this challenging task: a direct kernel regression from RM → G, and an indirect method with better extrapolation properties. We evaluate our methods on two crossdomain visual recognition benchmarks, where they perform close to the upper bound of full data domain adaptation. This demonstrates that data is not necessary for domain adaptation if a domain can be parametrically described.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)978-1-4673-8851-1
ISBN (Print)978-1-4673-8852-8
Publication statusPublished - 12 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016


Conference29th IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2016
Country/TerritoryUnited States
CityLas Vegas
Internet address


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