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arXiv · 2207.05252

Dynamic Proposals for Efficient Object Detection

Yiming Cui·Linjie Yang·Ding Liu
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Abstract

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.

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Yiming Cui, Linjie Yang, Ding Liu. 2022-07-12. Dynamic Proposals for Efficient Object Detection. https://arxiv.org/abs/2207.05252

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