During training, supervised object detection tries to correctly match the
predicted bounding boxes and associated classification scores to the ground
truth. This is essential to determine which predictions are to be pushed
towards which solutions, or to be discarded. Popular matching strategies
include matching to the closest ground truth box (mostly used in combination
with anchors), or matching via the Hungarian algorithm (mostly used in
anchor-free methods). Each of these strategies comes with its own properties,
underlying losses, and heuristics. We show how Unbalanced Optimal Transport
unifies these different approaches and opens a whole continuum of methods in
between. This allows for a finer selection of the desired properties.
Experimentally, we show that training an object detection model with Unbalanced
Optimal Transport is able to reach the state-of-the-art both in terms of
Average Precision and Average Recall as well as to provide a faster initial
convergence. The approach is well suited for GPU implementation, which proves
to be an advantage for large-scale models.

本文提出了一种新方法，使用不平衡最优输运（Unbalanced Optimal Transport）对目标检测模型进行训练，能够在 AP 和 AR 方面达到业内最好水平，并提供更快的初始收敛速度，适合于大规模模型的 GPU 实现。