Expandable-RCNN: toward high-efficiency incremental few-shot object detection

Expandable-RCNN: toward high-efficiency incremental few-shot object detection

This study aims at addressing the challenging incremental few-shot object detection (iFSOD) problem toward online adaptive detection. iFSOD targets to learn novel categories in a sequential manner, and eventually, the detection is performed on all learned categories. Moreover, only a few training sa...

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Bibliographic Details
Main Authors:	Yiting Li, Sichao Tian, Haiyue Zhu, Yeying Jin, Keqing Wang, Jun Ma, Cheng Xiang, Prahlad Vadakkepat
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2024-04-01
Series:	Frontiers in Artificial Intelligence
Subjects:	object detection few-shot learning zero-shot learning incremental learning long-tailed recognition
Online Access:	https://www.frontiersin.org/articles/10.3389/frai.2024.1377337/full

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