  We propose a dense object detector with an instance-wise sampling strategy,
named IQDet. Instead of using human prior sampling strategies, we first extract
the regional feature of each ground-truth to estimate the instance-wise quality
distribution. According to a mixture model in spatial dimensions, the
distribution is more noise-robust and adapted to the semantic pattern of each
instance. Based on the distribution, we propose a quality sampling strategy,
which automatically selects training samples in a probabilistic manner and
trains with more high-quality samples. Extensive experiments on MS COCO show
that our method steadily improves baseline by nearly 2.4 AP without bells and
whistles. Moreover, our best model achieves 51.6 AP, outperforming all existing
state-of-the-art one-stage detectors and it is completely cost-free in
inference time.
