  Deep learning-based dense object detectors have achieved great success in the
past few years and have been applied to numerous multimedia applications such
as video understanding. However, the current training pipeline for dense
detectors is compromised to lots of conjunctions that may not hold. In this
paper, we investigate three such important conjunctions: 1) only samples
assigned as positive in classification head are used to train the regression
head; 2) classification and regression share the same input feature and
computational fields defined by the parallel head architecture; and 3) samples
distributed in different feature pyramid layers are treated equally when
computing the loss. We first carry out a series of pilot experiments to show
disentangling such conjunctions can lead to persistent performance improvement.
Then, based on these findings, we propose Disentangled Dense Object Detector
(DDOD), in which simple and effective disentanglement mechanisms are designed
and integrated into the current state-of-the-art dense object detectors.
Extensive experiments on MS COCO benchmark show that our approach can lead to
2.0 mAP, 2.4 mAP and 2.2 mAP absolute improvements on RetinaNet, FCOS, and ATSS
baselines with negligible extra overhead. Notably, our best model reaches 55.0
mAP on the COCO test-dev set and 93.5 AP on the hard subset of WIDER FACE,
achieving new state-of-the-art performance on these two competitive benchmarks.
Code is available at https://github.com/zehuichen123/DDOD.
