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RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks

A general receptive field searching method for CNN.If your network has Conv with kernel larger than 1, RF-Next can further improve your model. The official implementation of:

TPAMI2022 paper: 'RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks'

CVPR2021 paper: 'Global2Local: Efficient Structure Search for Video Action Segmentation'

News

• 2022.12.18 RF-Convnext with An-easy-to-use RFConv is released. You can replace the Conv with the RFConv to support dilation rate search for any networks.
• 2022.12.18 Code for mmsegmentation is released.
• 2022.12.18 RF-ConvNext for large-scale semantic segmentation on ImageNet-S ImageNetSegModel.
• 2022.12.1 RF-Next is now in the official repo of MMCV, you can use rf-next in all MMlab codebases.
• 2022.6.11 Code for mmcv and mmdetection is released. ConvNext, PVT, Res2Net, HRNet are supported.
• 2022.4.24 RF-Next helps to achieve the 1st place (Team Feedback) in 2022 MegCup RAW image denoising. 2022.2.10 RF-Next improves the SOTA CNN model ConvNeXt on multiple tasks.
• 2021.1.1 RF-Next for Video Action Segmentation.

Introduction

Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields help to capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively search for receptive field combinations to replace hand-designed patterns? To answer this question, we propose to find better receptive field combinations through a global-to-local search scheme. Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further. The global search finds possible coarse combinations other than human-designed patterns. On top of the global search, we propose an expectation-guided iterative local search scheme to refine combinations effectively. Our RF-Next models, plugging receptive field search to various models, boost the performance on many tasks, e.g., temporal action segmentation, object detection, instance segmentation, and speech synthesis.

Applications and Codes

RF-Next supports many applications.

• Object detection and Instance segmentation in MMDet
• Semantic segmentation in MMSeg
• MMCV supports RF-Next
• Action segmentation Method G2L
• RF-ConvNext for large-scale semantic segmentation on ImageNet-S
• Speech synthesis

Citation

If you find this work or code is helpful in your research, please cite:

@article{gao2022rfnext,   
title={RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks},   
author={Gao, Shanghua and Li, Zhong-Yu and Han, Qi and Cheng, Ming-Ming and Wang, Liang},   
journal=TPAMI,   
year={2022} }

@inproceedings{gao2021global2local,
  title={Global2Local: Efficient Structure Search for Video Action Segmentation},
  author={Gao, Shang-Hua and Han, Qi and Li, Zhong-Yu and Peng, Pai and Wang, Liang and Cheng, Ming-Ming},
  booktitle=CVPR,
  year={2021}
}

License

The source code is free for research and education use only. Any comercial use should get formal permission first.

Contact

If you have any questions, feel free to E-mail Shang-Hua Gao (shgao(at)live.com)