  Object detection is a basic computer vision task to loccalize and categorize
objects in a given image. Most state-of-the-art detection methods utilize a
fixed number of proposals as an intermediate representation of object
candidates, which is unable to adapt to different computational constraints
during inference. In this paper, we propose a simple yet effective method which
is adaptive to different computational resources by generating dynamic
proposals for object detection. We first design a module to make a single
query-based model to be able to inference with different numbers of proposals.
Further, we extend it to a dynamic model to choose the number of proposals
according to the input image, greatly reducing computational costs. Our method
achieves significant speed-up across a wide range of detection models including
two-stage and query-based models while obtaining similar or even better
accuracy.
