Review for NeurIPS paper: Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

Weaknesses: The idea of predicting IoUs between predictions and ground-truths has already been explored by other works. It seems to me the main differences between this and prior works are: 1) Prior works such as IoU-Net still have a separate branch for classification but this one does not. The contribution of each modification to the final performance is unclear. Does the separate branch for classification hurt the performance? What if we just ignore the separate branch for classification in prior works?