  In this paper, we present a simple and parameter-efficient drop-in module for
one-stage object detectors like SSD when learning from scratch (i.e., without
pre-trained models). We call our module GFR (Gated Feature Reuse), which
exhibits two main advantages. First, we introduce a novel gate-controlled
prediction strategy enabled by Squeeze-and-Excitation to adaptively enhance or
attenuate supervision at different scales based on the input object size. As a
result, our model is more effective in detecting diverse sizes of objects.
Second, we propose a feature-pyramids structure to squeeze rich spatial and
semantic features into a single prediction layer, which strengthens feature
representation and reduces the number of parameters to learn. We apply the
proposed structure on DSOD and SSD detection frameworks, and evaluate the
performance on PASCAL VOC 2007, 2012 and COCO datasets. With fewer model
parameters, GFR-DSOD outperforms the baseline DSOD by 1.4%, 1.1%, 1.7% and
0.6%, respectively. GFR-SSD also outperforms the original SSD and SSD with
dense prediction by 3.6% and 2.8% on VOC 2007 dataset. Code is available at:
https://github.com/szq0214/GFR-DSOD .
