  Video object detection has been an important yet challenging topic in
computer vision. Traditional methods mainly focus on designing the image-level
or box-level feature propagation strategies to exploit temporal information.
This paper argues that with a more effective and efficient feature propagation
framework, video object detectors can gain improvement in terms of both
accuracy and speed. For this purpose, this paper studies object-level feature
propagation, and proposes an object query propagation (QueryProp) framework for
high-performance video object detection. The proposed QueryProp contains two
propagation strategies: 1) query propagation is performed from sparse key
frames to dense non-key frames to reduce the redundant computation on non-key
frames; 2) query propagation is performed from previous key frames to the
current key frame to improve feature representation by temporal context
modeling. To further facilitate query propagation, an adaptive propagation gate
is designed to achieve flexible key frame selection. We conduct extensive
experiments on the ImageNet VID dataset. QueryProp achieves comparable accuracy
with state-of-the-art methods and strikes a decent accuracy/speed trade-off.
Code is available at https://github.com/hf1995/QueryProp.
