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Moshe Lichtenstein

2 accepted papers

2021

StarNet: towards Weakly Supervised Few-Shot Object Detection

AAAI 2021technical

Few-shot detection and classification have advanced significantly in recent years. Yet, detection approaches require strong annotation (bounding boxes) both for pre-training and for adaptation to novel classes, and classification approaches rarely provide localization of objects in the scene. In thi…

2020

TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification

ECCV 2020poster

The field of Few-Shot Learning (FSL), or learning from very few (typically $1$ or $5$) examples per novel class (unseen during training), has received a lot of attention and significant performance advances in the recent literature. While number of techniques have been proposed for FSL, several fact…