  Sparse R-CNN is a recent strong object detection baseline by set prediction
on sparse, learnable proposal boxes and proposal features. In this work, we
propose to improve Sparse R-CNN with two dynamic designs. First, Sparse R-CNN
adopts a one-to-one label assignment scheme, where the Hungarian algorithm is
applied to match only one positive sample for each ground truth. Such
one-to-one assignment may not be optimal for the matching between the learned
proposal boxes and ground truths. To address this problem, we propose dynamic
label assignment (DLA) based on the optimal transport algorithm to assign
increasing positive samples in the iterative training stages of Sparse R-CNN.
We constrain the matching to be gradually looser in the sequential stages as
the later stage produces the refined proposals with improved precision. Second,
the learned proposal boxes and features remain fixed for different images in
the inference process of Sparse R-CNN. Motivated by dynamic convolution, we
propose dynamic proposal generation (DPG) to assemble multiple proposal experts
dynamically for providing better initial proposal boxes and features for the
consecutive training stages. DPG thereby can derive sample-dependent proposal
boxes and features for inference. Experiments demonstrate that our method,
named Dynamic Sparse R-CNN, can boost the strong Sparse R-CNN baseline with
different backbones for object detection. Particularly, Dynamic Sparse R-CNN
reaches the state-of-the-art 47.2% AP on the COCO 2017 validation set,
surpassing Sparse R-CNN by 2.2% AP with the same ResNet-50 backbone.
