  It is a common practice to exploit pyramidal feature representation to tackle
the problem of scale variation in object instances. However, most of them still
predict the objects in a certain range of scales based solely or mainly on a
single-level representation, yielding inferior detection performance. To this
end, we propose a novel adaptive feature selection module (AFSM), to
automatically learn the way to fuse multi-level representations in the channel
dimension, in a data-driven manner. It significantly improves the performance
of the detectors that have a feature pyramid structure, while introducing
nearly free inference overhead. Moreover, a class-aware sampling mechanism
(CASM) is proposed to tackle the class imbalance problem, by re-weighting the
sampling ratio to each of the training images, based on the statistical
characteristics of each class. This is crucial to improve the performance of
the minor classes. Experimental results demonstrate the effectiveness of the
proposed method, with 83.04% mAP at 15.96 FPS on the VOC dataset, and 39.48% AP
on the VisDrone-DET validation subset, respectively, outperforming other
state-of-the-art detectors considerably. The code is available at
https://github.com/ZeHuiGong/AFSM.git.
