Augmenting Anchors by the Detector Itself

TL;DR

The paper introduces AADI, a novel learning-based anchor augmentation method that converts anchor parameters from continuous to discrete space, improving object detection performance without adding extra parameters.

Contribution

AADI is a new method that alleviates anchor design issues by converting anchor parameters into a discrete space without increasing model complexity.

Findings

Achieves at least +2.4 box AP on Faster R-CNN

Achieves +2.2 box AP on Mask R-CNN

Achieves +0.9 box AP on Cascade Mask R-CNN

Abstract

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.