# PosNeg-Balanced Anchors with Aligned Features for Single-Shot Object Detection

## TL;DR

This paper presents a new single-shot object detection method that balances positive and negative anchors using an Anchor Promotion Module and a Feature Alignment Module, improving accuracy and speed.

## Contribution

The paper introduces the Anchor Promotion Module and Feature Alignment Module, enhancing anchor quality and feature alignment for better detection performance.

## Key findings

- Achieves 40.0% mAP on MS COCO test-dev set
- Runs at 28.6 fps, faster than comparable methods
- Effectively balances positive and negative anchors

## Abstract

We introduce a novel single-shot object detector to ease the imbalance of foreground-background class by suppressing the easy negatives while increasing the positives. To achieve this, we propose an Anchor Promotion Module (APM) which predicts the probability of each anchor as positive and adjusts their initial locations and shapes to promote both the quality and quantity of positive anchors. In addition, we design an efficient Feature Alignment Module (FAM) to extract aligned features for fitting the promoted anchors with the help of both the location and shape transformation information from the APM. We assemble the two proposed modules to the backbone of VGG-16 and ResNet-101 network with an encoder-decoder architecture. Extensive experiments on MS COCO well demonstrate our model performs competitively with alternative methods (40.0\% mAP on \textit{test-dev} set) and runs faster (28.6 \textit{fps}).