Dynamic R-CNN
Dynamic R-CNN
**Dynamic R-CNN** is an object detection method that adjusts the label assignment criteria (IoU threshold) and the shape of regression loss function (parameters of Smooth L1 Loss) automatically based on the statistics of proposals during training. The motivation is that in previous two-stage object detectors, there is an inconsistency problem between the fixed network settings and the dynamic training procedure. For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and thus are harmful to training high quality detectors.

It consists of two components: Dynamic Label Assignment and Dynamic Smooth L1 Loss, which are designed for the classification and regression branches, respectively. 

For Dynamic Label Assignment, we want our model to be discriminative for high IoU proposals, so we gradually adjust the IoU threshold for positive/negative samples based on the proposals distribution in the training procedure. Specifically, we set the threshold as the IoU of the proposal at a certain percentage since it can reflect the quality of the overall distribution. 

For Dynamic Smooth L1 Loss, we want to change the shape of the regression loss function to adaptively fit the distribution change of error and ensure the contribution of high quality samples to training. This is achieved by adjusting the $\beta$ in Smooth L1 Loss based on the error distribution of the regression loss function, in which $\beta$ actually controls the magnitude of the gradient of small errors.
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
https://paperswithcode.com/paper/dynamic-r-cnn-towards-high-quality-object
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training