The code repository for "RCNet: Reverse Feature Pyramid and Cross-scale Shift Network for Object Detection" (ACM MM'21)

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Deep LearningRCNet
Overview

RCNet: Reverse Feature Pyramid and Cross-scale Shift Network for Object Detection (ACM MM'21)

By Zhuofan Zong, Qianggang Cao, Biao Leng

Introduction

Feature pyramid networks (FPN) are widely exploited for multi-scale feature fusion in existing advanced object detection frameworks. Numerous previous works have developed various structures for bidirectional feature fusion, all of which are shown to improve the detection performance effectively. We observe that these complicated network structures require feature pyramids to be stacked in a fixed order, which introduces longer pipelines and reduces the inference speed. Moreover, semantics from non-adjacent levels are diluted in the feature pyramid since only features at adjacent pyramid levels are merged by the local fusion operation in a sequence manner. To address these issues, we propose a novel architecture named RCNet, which consists of Reverse Feature Pyramid (RevFP) and Cross-scale Shift Network (CSN). RevFP utilizes local bidirectional feature fusion to simplify the bidirectional pyramid inference pipeline. CSN directly propagates representations to both adjacent and non-adjacent levels to enable multi-scale features more correlative. Extensive experiments on the MS COCO dataset demonstrate RCNet can consistently bring significant improvements over both one-stage and two-stage detectors with subtle extra computational overhead. In particular, RetinaNet is boosted to 40.2 AP, which is 3.7 points higher than baseline, by replacing FPN with our proposed model. On COCO test-dev, RCNet can achieve very competitive performance with a single-model single-scale 50.5 AP.

Models

Pretrained models will be available.

Training and Testing

This project is based on mmdetection. Please follow mmdetection on how to install and use this repo. Config files can be found in configs/rcnet/.

Results on MS COCO

Detector Backbone Neck Lr schd mAP(val) mAP(test)
RetinaNet R50 RCNet 1x 40.2 -
ATSS R50 RCNet 1x 42.6 -
GFL R50 RCNet 1x 43.1 -
GFL R101 RCNet 2x 47.1 47.4
GFL X101-64x4d RCNet 2x 48.9 49.2
GFL X101-64x4d-DCN RCNet 2x 50.2 50.5

Citations

If you find RCNet useful in your research, please consider citing:

@inproceedings{zong2021rcnet,
author = {Zong, Zhuofan and Cao, Qianggang and Leng, Biao},
title = {RCNet: Reverse Feature Pyramid and Cross-scale Shift Network for Object Detection},
booktitle = {ACM MM},
pages = {5637–5645},
year = {2021}
}

License

This project is released under the Apache 2.0 license

Owner
TempleX
TempleX
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