In this work we develop a few-shot object detector that can learn to detect novel objects from only a few annotated examples Our proposed model leverages fully labeled base classes and quickly adapts to novel classes, using a meta feature learner and a reweighting module within a one-stage detec- tion architecture
Kang Few Shot Object Detection via Feature Reweighting ICCV paper
Few-shot Object Detection via Feature Reweighting Bingyi Kang1*, Zhuang Liu2 ∗, and quickly adapts to novel classes, using a meta feature learner and a
cremental few-shot object detection problem in the context of deep query images I by using the feature extractor (Eq (4)) and Feature-Reweight [22] 5 6
PerezEtAl CVPR
There are several early at- tempts at few-shot object detection using meta- learning Kang et al (2019) and Yan et al (2019) apply feature re-weighting schemes to
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[10] present a new model using a meta feature learner and a re-weighting module to fast adjust contributions of the basic features to the detection of new classes
ac ec ae c ae e Paper
reweighting module to existing object detection networks [23, 64] Though these shot object detection network using the same loss function: L = Lrpn + Lcls +
Few shot object detection (FSD) is gaining popularity, enhanced by the deep learn- by multiplying the last feature map by a number of feature reweighting coeffi- 10-shot tasks, using COCO for training and PASCAL VOC for evaluation
Keywords: few shot classification · feature reweighting · meta-learning 1 Introduction In recent construct an AND-OR graph using patches to represent each character object experience that is useful for few shot recognition task In [7], the
Few-shot Object Detection via Feature Reweighting. Bingyi Kang1* Zhuang Liu2?
Oct 21 2019 The feature learner extracts meta features that are generalizable to detect novel object classes
Few-shot Object Detection via Feature Reweighting. Bingyi Kang1* Zhuang Liu2?
Jul 31 2022 Abstract: Current Synthetic Aperture Radar (SAR) image object detection methods require huge amounts of annotated data and can only detect ...
Supplementary: Few-Shot Object Detection via Association to a distortion of real feature representation of novel classes. ... via feature reweighting.
Few-shot object detection via feature reweighting. In. 2019 IEEE/CVF International Conference on Computer Vi- sion (ICCV) pages 8419–8428
Jul 31 2022 Some meta-learning-based methods
As a result the feature space of a novel class will have an incompact intra-class structure that scatters across feature spaces of other classes
Few-Shot Object Detection via Feature Reweighting