@@ -40,7 +40,14 @@ multiple stages for localization and counting, i.e., S_{1},..., S_{N} are cascad
instances enclosed in the bounding box, where N is the total number of stages. For more detailed definitions, please refer to the [paper](http://arxiv.org/abs/2003.08230).
The counting accuracy threshold for the positive/negative sample generation is determined by the architecture design of CLCNet, which is described as follows.
We use the same architecture and configuration as CrCNN for the box-regression and box-classification layers. For the instance counting layer, a direct strategy is to use a FC layer to regress a floating point number, indicating the number of instances, called {\em count-regression strategy}. However, the numbers of instances enclosed in the bounding boxes are integers, leading challenges for the network to regress accurately. For example, if the ground-truth numbers of instances are $4$ and $5$ for two bounding boxes, and both of the predictions are $4.5$, it is difficult for the network to choose the right direction in the training phase. To that end, we design a classification strategy to handle such issue, called {\em count-classification strategy}. Specifically, we assume the maximal number of instances is $\alpha$ and construct $\alpha$ bins to indicate the number of instances. Thus, the counting task is formulated as the multi-class classification task, which use a FC layer to determine the bin index for instance number.
We use the same architecture and configuration as Cascade R-CNN for the box-regression and box-classification layers. For the instance counting layer,
a direct strategy is to use a FC layer to regress a floating point number, indicating the number of instances, called *count-regression strategy*.
However, the numbers of instances enclosed in the bounding boxes are integers, leading challenges for the network to regress accurately.
For example, if the ground-truth numbers of instances are 4 and 5 for two bounding boxes, and both of the predictions are 4.5,
it is difficult for the network to choose the right direction in the training phase. To that end, we design a classification strategy to handle such issue,
called *count-classification strategy*.
Specifically, we assume the maximal number of instances is *m* and construct *m* bins to indicate the number of instances.
Thus, the counting task is formulated as the multi-class classification task, which use a FC layer to determine the bin index for instance number.
## Citation
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