Augmenting Anchors by the Detector Itself
- URL: http://arxiv.org/abs/2105.14086v1
- Date: Fri, 28 May 2021 20:11:08 GMT
- Title: Augmenting Anchors by the Detector Itself
- Authors: Xiaopei Wan, Shengjie Chen, Yujiu Yang, Zhenhua Guo, Fangbo Tao
- Abstract summary: We propose a gradient-free anchor augmentation method named AADI, which means Augmenting Anchors by the Detector Itself.
AADI is not an anchor-free method, but it converts the scale and aspect ratio of anchors from a continuous space to a discrete space.
Extensive experiments on COCO dataset show that AADI has obvious advantages for both two-stage and single-stage methods.
- Score: 14.6595323571382
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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 method. In
this paper, we propose a gradient-free anchor augmentation method named AADI,
which means Augmenting Anchors by the Detector Itself. AADI is not an
anchor-free method, but it converts 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 does not add any parameters or
hyper-parameters, which is beneficial for future research and downstream tasks.
Extensive experiments on COCO dataset show that AADI has obvious advantages for
both two-stage and single-stage methods, specifically, AADI achieves at least
2.1 AP improvements on Faster R-CNN and 1.6 AP improvements on RetinaNet, using
ResNet-50 model. We hope that this simple and cost-efficient method can be
widely used in object detection.
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