StarNet: towards Weakly Supervised Few-Shot Object Detection

Leonid Karlinsky; Joseph Shtok; Amit Alfassy; Moshe Lichtenstein; Sivan Harary; Eli Schwartz; Sivan Doveh; Prasanna Sattigeri; Rogerio Feris; Alexander Bronstein; Raja Giryes

arXiv · 2003.06798

StarNet: towards Weakly Supervised Few-Shot Object Detection

Leonid Karlinsky·Joseph Shtok·Amit Alfassy·Moshe Lichtenstein·Sivan Harary·Eli Schwartz·Sivan Doveh·Prasanna Sattigeri·Rogerio Feris·Alexander Bronstein·Raja Giryes

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Abstract

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 this paper, we introduce StarNet - a few-shot model featuring an end-to-end differentiable non-parametric star-model detection and classification head. Through this head, the backbone is meta-trained using only image-level labels to produce good features for jointly localizing and classifying previously unseen categories of few-shot test tasks using a star-model that geometrically matches between the query and support images (to find corresponding object instances). Being a few-shot detector, StarNet does not require any bounding box annotations, neither during pre-training nor for novel classes adaptation. It can thus be applied to the previously unexplored and challenging task of Weakly Supervised Few-Shot Object Detection (WS-FSOD), where it attains significant improvements over the baselines. In addition, StarNet shows significant gains on few-shot classification benchmarks that are less cropped around the objects (where object localization is key).

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Leonid Karlinsky, Joseph Shtok, Amit Alfassy, Moshe Lichtenstein, Sivan Harary, Eli Schwartz, Sivan Doveh, Prasanna Sattigeri, Rogerio Feris, Alexander Bronstein, Raja Giryes. 2020-03-15. StarNet: towards Weakly Supervised Few-Shot Object Detection. https://arxiv.org/abs/2003.06798

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