We motivate and present feature selective anchor-free (FSAF) module, a simple
and effective building block for single-shot object detectors. It can be
plugged into single-shot detectors with feature pyramid structure. The FSAF
module addresses two limitations brought up by the conventional anchor-based
detection: 1) heuristic-guided feature selection; 2) overlap-based anchor
sampling. The general concept of the FSAF module is online feature selection
applied to the training of multi-level anchor-free branches. Specifically, an
anchor-free branch is attached to each level of the feature pyramid, allowing
box encoding and decoding in the anchor-free manner at an arbitrary level.
During training, we dynamically assign each instance to the most suitable
feature level. At the time of inference, the FSAF module can work jointly with
anchor-based branches by outputting predictions in parallel. We instantiate
this concept with simple implementations of anchor-free branches and online
feature selection strategy. Experimental results on the COCO detection track
show that our FSAF module performs better than anchor-based counterparts while
being faster. When working jointly with anchor-based branches, the FSAF module
robustly improves the baseline RetinaNet by a large margin under various
settings, while introducing nearly free inference overhead. And the resulting
best model can achieve a state-of-the-art 44.6% mAP, outperforming all existing
single-shot detectors on COCO.

本文提出了一种基于特征选择锚点的：Feature Selective Anchor-Free (FSAF) 模块，它是一种可以插入特征金字塔结构的单镜头目标检测器构建模块，通过多级无锚点分支来在线选择特征以训练模型，并具有较高的检测准确性和速度。实验结果表明，FSAF 模块与 RetinaNet 效果相比显著提高，在 COCO 数据集上实现了 44.6% 的 mAP，成为目前所有现有单镜头检测器中表现最佳的模型。