4.58% to 72.44% using only 5 training images each with 5 bounding boxes. On the Drink35 dataset for shelf product counting, the mAP@0.5 is improved from 14.16% to 53.73% using 10 training images each with 5 bounding boxes.

Related Work
============

Object Counting
---------------

The approaches towards object counting can be roughly categorized into three categories: regression-based approach, density-based approach, and detection-based approach.

### Regression-based Approach

predicts instance count directly based on global regressors with image features [@ChanLV08; @ChenGXL13; @ChenLGX12; @KongGT06]. For example, in [@ChanLV08], the video sequence is first segmented into different components of homogeneous motion, and a Gaussian process regression is learned for each segmented region to count the number of instances. Cumulative attribute representation [@ChenGXL13] was proposed and used to learn the regressor to handle the imbalanced training data. Inter-dependent features are used to mine the spatially importance among different region to learn the number of count [@ChenLGX12]. Feature normalization is taken into account to deal with perspective projections in [@KongGT06]. With the advantage of the deep learning,  [@MundhenkKSB16] estimate the number of cars using CNNs. These regression-based approaches provide no clue of individual location of each object, which limits its potential applications.

### Density-based Approach

first 