  Video object detection is a fundamental problem in computer vision and has a
wide spectrum of applications. Based on deep networks, video object detection
is actively studied for pushing the limits of detection speed and accuracy. To
reduce the computation cost, we sparsely sample key frames in video and treat
the rest frames are non-key frames; a large and deep network is used to extract
features for key frames and a tiny network is used for non-key frames. To
enhance the features of non-key frames, we propose a novel short-term feature
aggregation method to propagate the rich information in key frame features to
non-key frame features in a fast way. The fast feature aggregation is enabled
by the freely available motion cues in compressed videos. Further, key frame
features are also aggregated based on optical flow. The propagated deep
features are then integrated with the directly extracted features for object
detection. The feature extraction and feature integration parameters are
optimized in an end-to-end manner. The proposed video object detection network
is evaluated on the large-scale ImageNet VID benchmark and achieves 77.2\% mAP,
which is on-par with state-of-the-art accuracy, at the speed of 30 FPS using a
Titan X GPU. The source codes are available at
\url{https://github.com/hustvl/LSFA}.
