mmsegmentation/configs/dnlnet

• DNLNet
• • Introduction
  • Abstract
  • Citation
  • Citation
  • Results and models (in progress)
  • • Cityscapes
    • ADE20K

DNLNet

Disentangled Non-Local Neural Networks

Introduction

Official Repo

Code Snippet

Abstract

The non-local block is a popular module for strengthening the context modeling ability of a regular convolutional neural network. This paper first studies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship between two pixels and a unary term representing the saliency of every pixel. We also observe that the two terms trained alone tend to model different visual clues, e.g. the whitened pairwise term learns within-region relationships while the unary term learns salient boundaries. However, the two terms are tightly coupled in the non-local block, which hinders the learning of each. Based on these findings, we present the disentangled non-local block, where the two terms are decoupled to facilitate learning for both terms. We demonstrate the effectiveness of the decoupled design on various tasks, such as semantic segmentation on Cityscapes, ADE20K and PASCAL Context, object detection on COCO, and action recognition on Kinetics.

Citation

This example is to reproduce "Disentangled Non-Local Neural Networks" for semantic segmentation. It is still in progress.

Citation

@misc{yin2020disentangled,
    title={Disentangled Non-Local Neural Networks},
    author={Minghao Yin and Zhuliang Yao and Yue Cao and Xiu Li and Zheng Zhang and Stephen Lin and Han Hu},
    year={2020},
    booktitle={ECCV}
}

Results and models (in progress)

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DNLNet	R-50-D8	512x1024	40000	7.3	2.56	78.61	-	config	model | log
DNLNet	R-101-D8	512x1024	40000	10.9	1.96	78.31	-	config	model | log
DNLNet	R-50-D8	769x769	40000	9.2	1.50	78.44	80.27	config	model | log
DNLNet	R-101-D8	769x769	40000	12.6	1.02	76.39	77.77	config	model | log
DNLNet	R-50-D8	512x1024	80000	-	-	79.33	-	config	model | log
DNLNet	R-101-D8	512x1024	80000	-	-	80.41	-	config	model | log
DNLNet	R-50-D8	769x769	80000	-	-	79.36	80.70	config	model | log
DNLNet	R-101-D8	769x769	80000	-	-	79.41	80.68	config	model | log

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DNLNet	R-50-D8	512x512	80000	8.8	20.66	41.76	42.99	config	model | log
DNLNet	R-101-D8	512x512	80000	12.8	12.54	43.76	44.91	config	model | log
DNLNet	R-50-D8	512x512	160000	-	-	41.87	43.01	config	model | log
DNLNet	R-101-D8	512x512	160000	-	-	44.25	45.78	config	model | log