  This paper aims to tackle the challenging task of one-shot object counting.
Given an image containing novel, previously unseen category objects, the goal
of the task is to count all instances in the desired category with only one
supporting bounding box example. To this end, we propose a counting model by
which you only need to Look At One instance (LaoNet). First, a feature
correlation module combines the Self-Attention and Correlative-Attention
modules to learn both inner-relations and inter-relations. It enables the
network to be robust to the inconsistency of rotations and sizes among
different instances. Second, a Scale Aggregation mechanism is designed to help
extract features with different scale information. Compared with existing
few-shot counting methods, LaoNet achieves state-of-the-art results while
learning with a high convergence speed. The code will be available soon.
