Incremental Generalized Category Discovery

Bingchen Zhao, Oisin Mac Aodha

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

Abstract / Description of output

We explore the problem of Incremental Generalized Category Discovery (IGCD). This is a challenging category incremental learning setting where the goal is to develop models that can correctly categorize images from previously seen categories, in addition to discovering novel ones. Learning is performed over a series of time steps where the model obtains new labeled and unlabeled data, and discards old data, at each iteration. The difficulty of the problem is compounded in our generalized setting as the unlabeled data can contain images from categories that may or may not have been observed before. We present a new method for IGCD which combines non-parametric categorization with efficient image sampling to mitigate catastrophic forgetting. To quantify performance, we propose a new benchmark dataset named iNatIGCD that is motivated by a real-world fine-grained visual categorization task. In our experiments we outperform existing related methods.
Original languageEnglish
Title of host publication2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Number of pages11
ISBN (Electronic)979-8-3503-0718-4
ISBN (Print)979-8-3503-0719-1
Publication statusPublished - 15 Jan 2024
EventInternational Conference on Computer Vision 2023 - Paris, France
Duration: 2 Oct 20236 Oct 2023

Publication series

NameInternational Conference on Computer Vision (ICCV)
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504


ConferenceInternational Conference on Computer Vision 2023
Abbreviated titleICCV 2023
Internet address


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