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Resembling the rapid learning capability of human, few-shot learning empowersvision systems to understand new concepts by training with few samples. Leadingapproaches derived from meta-learning on images with a single visual object.Obfuscated by a complex background and multiple objects in one image, they arehard to promote the research of few-shot object detection/segmentation. In thiswork, we present a flexible and general methodology to achieve these tasks. Ourwork extends Faster /Mask R-CNN by proposing meta-learning over RoI(Region-of-Interest) features instead of a full image feature. This simplespirit disentangles multi-object information merged with the background,without bells and whistles, enabling Faster /Mask R-CNN turn into ameta-learner to achieve the tasks. Specifically, we introduce a Predictor-headRemodeling Network (PRN) that shares its main backbone with Faster /Mask R-CNN.PRN receives images containing few-shot objects with their bounding boxes ormasks to infer their class attentive vectors. The vectors take channel-wisesoft-attention on RoI features, remodeling those R-CNN predictor heads todetect or segment the objects that are consistent with the classes thesevectors represent. In our experiments, Meta R-CNN yields the state of the artin few-shot object detection and improves few-shot object segmentation by MaskR-CNN.

Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning