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

QueryProp: Object Query Propagation for High-Performance Video Object Detection