Incremental Few-Shot Object Detection

Juan Manuel Perez-Rua, Xiatian Zhu, Timothy Hospedales, Tao Xiang

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

Abstract / Description of output

Most existing object detection methods rely on the availability of abundant labelled training samples per class and offline model training in a batch mode. These requirements substantially limit their scalability to open-ended accommodation of novel classes with limited labelled training data. We present a study aiming to go beyond these limitations by considering the Incremental Few-Shot Detection (iFSD) problem setting, where new classes must be registered incrementally (without revisiting base classes) and with few examples. To this end we propose OpeNended Centre nEt (ONCE), a detector designed for incrementally learning to detect novel class objects with few examples. This is achieved by an elegant adaptation of the CentreNet detector to the few-shot learning scenario, and meta-learning a class-specific code generator model for registering novel classes. ONCE fully respects the incremental learning paradigm, with novel class
registration requiring only a single forward pass of few-shot training samples, and no access to base classes – thus making it suitable for deployment on embedded devices. Extensive experiments conducted on both the standard object detection and fashion landmark detection tasks show the feasibility of iFSD for the first time, opening an interesting and very important line of research.
Original languageEnglish
Title of host publication2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Place of PublicationSeattle, WA, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)978-1-7281-7168-5
ISBN (Print)978-1-7281-7169-2
Publication statusPublished - 5 Aug 2020
EventIEEE Conference on Computer Vision and Pattern Recognition 2020 - Seattle, United States
Duration: 16 Jun 202018 Jun 2020

Publication series

ISSN (Print)1063-6919
ISSN (Electronic)2575-7075


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2020
Abbreviated titleCVPR 2020
Country/TerritoryUnited States
Internet address


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