Few Exemplar Annotations
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Introduction
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Object counting is to count the number of object instances in a single image or video sequence. It has many real-world applications such as traffic flow monitoring, crowdedness estimation, and product counting.

Existing approaches towards the counting problem can be roughly categorized as regression-based approach [@AnLV07; @ChanLV08; @ChenGXL13; @ChenLGX12; @KongGT06], density-based approach [@ZhangLWY15; @ArtetaLNZ14; @RodriguezLSA11] and detection-based approach [@HsiehLH17; @KamenetskyS15; @MoranduzzoM14]. The regression-based approach directly learns a mapping from the image to the number of instances. In contrast, the density-based approach first estimates a density map and then aggregates the density information to get the number of instances. Both categories of approaches provide little information of exact instance location, whereas the detection-based approach is capable of detecting the location of each individual instance and thus making the counting result more explainable. Due to this advantage, we mainly focus on the detection-based approach in this work.

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          ![Different training image settings in detection-based object counting. The label names are shown in bottom left []{data-label="fig:isl"}](figs/CARPK_fsl.jpg "fig:"){width="\thirdsplitwidth\linewidth"}                  ![Different training image settings in detection-based object counting. The label names are shown in bottom left []{data-label="fig:isl"}](figs/CARPK_wsl.jpg "fig:"){width="\thirdsplitwidth\linewidth"}                  ![Different training image settings in detection-based object counting. The label 