The paper has clearly mentioned the classification and regression losses are identical to the RPN network in the Faster RCNN . Can someone explain the Mask Loss function . How the use FCN to improve ?
2 Answers
The multi-task loss function of Mask R-CNN combines the loss of classification, localization and segmentation mask: L=Lcls+Lbox+Lmask, where Lcls and Lbox are same as in Faster R-CNN.
The mask branch generates a mask of dimension m x m for each RoI and each class; K classes in total. Thus, the total output is of size K⋅m^2
Because the model is trying to learn a mask for each class, there is no competition among classes for generating masks.
Lmask :
is defined as the average binary cross-entropy loss, only including k-th mask if the region is associated with the ground truth class k.
where yij is the label of a cell (i, j) in the true mask for the region of size m x m; y^kij is the predicted value of the same cell in the mask learned for the ground-truth class k.
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