Feature Selective Anchor-Free Module for Single-Shot Object Detection
Abstract
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 singleshot
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.