Dynamic Proposals for Efficient Object Detection
This work addresses computational inefficiency in object detection for computer vision applications, offering an incremental improvement over existing methods.
The paper tackles the inefficiency of fixed proposal numbers in object detection by introducing a method that generates dynamic proposals, achieving significant speed-up across various detection models while maintaining or improving accuracy.
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.