Weak supervision

Learning to Learn Localization

The problem of how to recognize and localize objects on images is well-studied and gained very promising result in computer vision research area; specifically after the neural networks are widely used in the field. However, these researches generally requires fully supervised setting which is the use of bounding box annotations of class examples. Such a need to reach high performance is generally very costly. Instead, we propose a framework in which we don’t directly learn the localization of specific classes; the model learns to how to learn localization.