  Current anchor-free object detectors label all the features that spatially
fall inside a predefined central region of a ground-truth box as positive. This
approach causes label noise during training, since some of these positively
labeled features may be on the background or an occluder object, or they are
simply not discriminative features. In this paper, we propose a new labeling
strategy aimed to reduce the label noise in anchor-free detectors. We sum-pool
predictions stemming from individual features into a single prediction. This
allows the model to reduce the contributions of non-discriminatory features
during training. We develop a new one-stage, anchor-free object detector,
PPDet, to employ this labeling strategy during training and a similar
prediction pooling method during inference. On the COCO dataset, PPDet achieves
the best performance among anchor-free top-down detectors and performs on-par
with the other state-of-the-art methods. It also outperforms all major
one-stage and two-stage methods in small object detection (${AP}_{S}$ $31.4$).
Code is available at https://github.com/nerminsamet/ppdet
