  Resembling the rapid learning capability of human, few-shot learning empowers
vision systems to understand new concepts by training with few samples. Leading
approaches derived from meta-learning on images with a single visual object.
Obfuscated by a complex background and multiple objects in one image, they are
hard to promote the research of few-shot object detection/segmentation. In this
work, we present a flexible and general methodology to achieve these tasks. Our
work extends Faster /Mask R-CNN by proposing meta-learning over RoI
(Region-of-Interest) features instead of a full image feature. This simple
spirit disentangles multi-object information merged with the background,
without bells and whistles, enabling Faster /Mask R-CNN turn into a
meta-learner to achieve the tasks. Specifically, we introduce a Predictor-head
Remodeling Network (PRN) that shares its main backbone with Faster /Mask R-CNN.
PRN receives images containing few-shot objects with their bounding boxes or
masks to infer their class attentive vectors. The vectors take channel-wise
soft-attention on RoI features, remodeling those R-CNN predictor heads to
detect or segment the objects that are consistent with the classes these
vectors represent. In our experiments, Meta R-CNN yields the state of the art
in few-shot object detection and improves few-shot object segmentation by Mask
R-CNN.
