Augmenting Anchors by the Detector Itself

Usually, it is difficult to determine the scale and aspect ratio of anchors for anchor-based object detection methods. Current state-of-the-art object detectors either determine anchor parameters according to objects' shape and scale in a dataset, or avoid this problem by utilizing anchor-free methods, however, the former scheme is dataset-specific and the latter methods could not get better performance than the former ones. In this paper, we propose a novel anchor augmentation method named AADI, which means Augmenting Anchors by the Detector Itself. AADI is not an anchor-free method, instead, it can convert the scale and aspect ratio of anchors from a continuous space to a discrete space, which greatly alleviates the problem of anchors' designation. Furthermore, AADI is a learning-based anchor augmentation method, but it does not add any parameters or hyper-parameters, which is beneficial for research and downstream tasks. Extensive experiments on COCO dataset demonstrate the effectiveness of AADI, specifically, AADI achieves significant performance boosts on many state-of-the-art object detectors (eg. at least +2. 4 box AP on Faster R-CNN, +2. 2 box AP on Mask R-CNN, and +0. 9 box AP on Cascade Mask R-CNN). We hope that this simple and cost-efficient method can be widely used in object detection. Code and models are available at https: //github. com/WanXiaopei/aadi.

Authors

• Xiaopei Wan
• Guoqiu Li Alibaba Group
• Yujiu Yang
• Zhenhua Guo Inspur Electronic Information Industry Co.,Ltd.

Keywords

• Computer Vision: Recognition (object detection, categorization)