Dynamic Sparse R-CNN. (arXiv:2205.02101v1 [cs.CV])

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, …

arxiv cnn cv r-cnn