Being Aware of Localization Accuracy By Generating Predicted-IoU-Guided
  Quality Scores
Pengfei Liu Weibo Wang Yuhan Guo Jiubin Tan
Localization Quality Estimation (LQE) helps to improve detection performance
as it benefits post processing through jointly considering classification score
and localization accuracy. In this perspective, for further leveraging the
close relationship between localization accuracy and IoU
(Intersection-Over-Union), and for depressing those inconsistent predictions,
we designed an elegant LQE branch to acquire localization quality score guided
by predicted IoU. Distinctly, for alleviating the inconsistency of
classification score and localization quality during training and inference,
under which some predictions with low classification scores but high LQE scores
will impair the performance, instead of separately and independently setting,
we embedded LQE branch into classification branch, producing a joint
classification-localization-quality representation. Then a novel one stage
detector termed CLQ is proposed. Extensive experiments show that CLQ achieves
state-of-the-arts' performance at an accuracy of 47.8 AP and a speed of 11.5
fps with ResNeXt-101 as backbone on COCO test-dev. Finally, we extend CLQ to
ATSS, producing a reliable 1.2 AP gain, showing our model's strong adaptability
and scalability. Codes are released at https://github.com/PanffeeReal/CLQ.