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Incremental Few-Shot Object Detection
[article]
2020
arXiv
pre-print
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
arXiv:2003.04668v2
fatcat:kzmyl25s4vdxzbwmgh4cvcwufe