Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian

Yongxin Yang, Timothy Hospedales

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

Abstract

Most visual recognition methods implicitly assume the data distribution remains unchanged from training to testing. However, in practice domain shift often exists, where real-world factors such as lighting and sensor type change between train and test, and classifiers do not generalise from source to target domains. It is impractical to train separate models for all possible situations because collecting and labelling the data is expensive. Domain adaptation algorithms aim to ameliorate domain shift, allowing a model trained on a source to perform well on a different target domain. However, even for the setting of unsupervised domain adaptation, where the target domain is unlabelled, collecting data for every possible target domain is still costly. In this paper, we propose a new domain adaptation method that has no need to access either data or labels of the target domain when it can be described by a parametrised vector and there exits several related source domains within the same parametric space. It greatly reduces the burden of data collection and annotation, and our experiments show some promising results
Original languageEnglish
Title of host publication1st International Workshop on Differential Geometry in Computer Vision (Diff-CV)
Number of pages12
DOIs
Publication statusPublished - 2015
Event1st International Workshop on DIFFerential Geometry in Computer Vision for Analysis of Shapes, Images and Trajectories (DIFF-CV) - Faraday Lecture Theatre, Swansea, United Kingdom
Duration: 10 Sep 2015 → …
http://bmvc2015.swan.ac.uk/?p=2681

Conference

Conference1st International Workshop on DIFFerential Geometry in Computer Vision for Analysis of Shapes, Images and Trajectories (DIFF-CV)
Abbreviated titleDIFF-CV 2015
Country/TerritoryUnited Kingdom
CitySwansea
Period10/09/15 → …
Internet address

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