The code for Expectation-Maximization Attention Networks for Semantic Segmentation (ICCV'2019 Oral)

Related tags

Deep LearningEMANet
Overview

EMANet

News

  • The bug in loading the pretrained model is now fixed. I have updated the .pth. To use it, download it again.
  • EMANet-101 gets 80.99 on the PASCAL VOC dataset (Thanks for Sensetimes' server). So, with a classic backbone(ResNet) instead of some newest ones(WideResNet, HRNet), EMANet still achieves the top performance.
  • EMANet-101 (OHEM) gets 81.14 in mIoU on Cityscapes val using single-scale inference, and 81.9 on test server with multi-scale inference.

Background

This repository is for Expectation-Maximization Attention Networks for Semantic Segmentation (to appear in ICCV 2019, Oral presentation),

by Xia Li, Zhisheng Zhong, Jianlong Wu, Yibo Yang, Zhouchen Lin and Hong Liu from Peking University.

The source code is now available!

citation

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

@inproceedings{li19,
    author={Xia Li and Zhisheng Zhong and Jianlong Wu and Yibo Yang and Zhouchen Lin and Hong Liu},
    title={Expectation-Maximization Attention Networks for Semantic Segmentation},
    booktitle={International Conference on Computer Vision},   
    year={2019},   
}

table of contents

Introduction

Self-attention mechanism has been widely used for various tasks. It is designed to compute the representation of each position by a weighted sum of the features at all positions. Thus, it can capture long-range relations for computer vision tasks. However, it is computationally consuming. Since the attention maps are computed w.r.t all other positions. In this paper, we formulate the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed. By a weighted summation upon these bases, the resulting representation is low-rank and deprecates noisy information from the input. The proposed Expectation-Maximization Attention (EMA) module is robust to the variance of input and is also friendly in memory and computation. Moreover, we set up the bases maintenance and normalization methods to stabilize its training procedure. We conduct extensive experiments on popular semantic segmentation benchmarks including PASCAL VOC, PASCAL Context, and COCO Stuff, on which we set new records. EMA Unit

Design

As so many peers have starred at this repo, I feel the great pressure, and try to release the code with high quality. That's why I didn't release it until today (Aug, 22, 2018). It's known that the design of the code structure is not an easy thing. Different designs are suitable for different usage. Here, I aim at making research on Semantic Segmentation, especially on PASCAL VOC, more easier. So, I delete necessary encapsulation as much as possible, and leave over less than 10 python files. To be honest, the global variables in settings are not a good design for large project. But for research, it offers great flexibility. So, hope you can understand that

For research, I recommand seperatting each experiment with a folder. Each folder contains the whole project, and should be named as the experiment settings, such as 'EMANet101.moving_avg.l2norm.3stages'. Through this, you can keep tracks of all the experiments, and find their differences just by the 'diff' command.

Usage

  1. Install the libraries listed in the 'requirements.txt'
  2. Downloads images and labels of PASCAL VOC and SBD, decompress them together.
  3. Downloads the pretrained ResNet50 and ResNet101, unzip them, and put into the 'models' folder.
  4. Change the 'DATA_ROOT' in settings.py to where you place the dataset.
  5. Run sh clean.sh to clear the models and logs from the last experiment.
  6. Run python train.py for training and sh tensorboard.sh for visualization on your browser.
  7. Or you can download the pretraind model, put into the 'models' folder, and skip step 6.
  8. Run python eval.py for validation

Ablation Studies

The following results are referred from the paper. For this repo, it's not strange to get even higer performance. If so, I'd like you share it in the issue. By now, this repo only provides the SS inference. I may release the code for MS and Flip latter.

Tab 1. Detailed comparisons with Deeplabs. All results are achieved with the backbone ResNet-101 and output stride 8. The FLOPs and memory are computed with the input size 513×513. SS: Single scale input during test. MS: Multi-scale input. Flip: Adding left-right flipped input. EMANet (256) and EMANet (512) represent EMANet withthe number of input channels for EMA as 256 and 512, respectively.

Method SS MS+Flip FLOPs Memory Params
ResNet-101 - - 190.6G 2.603G 42.6M
DeeplabV3 78.51 79.77 +63.4G +66.0M +15.5M
DeeplabV3+ 79.35 80.57 +84.1G +99.3M +16.3M
PSANet 78.51 79.77 +56.3G +59.4M +18.5M
EMANet(256) 79.73 80.94 +21.1G +12.3M +4.87M
EMANet(512) 80.05 81.32 +43.1G +22.1M +10.0M

To be note, the majority overheads of EMANets come from the 3x3 convs before and after the EMA Module. As for the EMA Module itself, its computation is only 1/3 of a 3x3 conv's, and its parameter number is even smaller than a 1x1 conv.

Comparisons with SOTAs

Note that, for validation on the 'val' set, you just have to train 30k on the 'trainaug' set. But for test on the evaluation server, you should first pretrain on COCO, and then 30k on 'trainaug', and another 30k on the 'trainval' set.

Tab 2. Comparisons on the PASCAL VOC test dataset.

Method Backbone mIoU(%)
GCN ResNet-152 83.6
RefineNet ResNet-152 84.2
Wide ResNet WideResNet-38 84.9
PSPNet ResNet-101 85.4
DeeplabV3 ResNet-101 85.7
PSANet ResNet-101 85.7
EncNet ResNet-101 85.9
DFN ResNet-101 86.2
Exfuse ResNet-101 86.2
IDW-CNN ResNet-101 86.3
SDN DenseNet-161 86.6
DIS ResNet-101 86.8
EMANet101 ResNet-101 87.7
DeeplabV3+ Xception-65 87.8
Exfuse ResNeXt-131 87.9
MSCI ResNet-152 88.0
EMANet152 ResNet-152 88.2

Code Borrowed From

RESCAN

Pytorch-Encoding

Synchronized-BN

Dense Contrastive Learning (DenseCL) for self-supervised representation learning, CVPR 2021.

Dense Contrastive Learning for Self-Supervised Visual Pre-Training This project hosts the code for implementing the DenseCL algorithm for se

Xinlong Wang 491 Jan 03, 2023
Source code for the ACL-IJCNLP 2021 paper entitled "T-DNA: Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation" by Shizhe Diao et al.

T-DNA Source code for the ACL-IJCNLP 2021 paper entitled Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adapta

shizhediao 17 Dec 22, 2022
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

Deep Exemplar-based Video Colorization (Pytorch Implementation) Paper | Pretrained Model | Youtube video 🔥 | Colab demo Deep Exemplar-based Video Col

Bo Zhang 253 Dec 27, 2022
UMich 500-Level Mobile Robotics Course

MOBILE ROBOTICS: METHODS & ALGORITHMS - WINTER 2022 University of Michigan - NA 568/EECS 568/ROB 530 For slides, lecture notes, and example codes, see

393 Dec 29, 2022
Code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks

Biomedical Entity Linking This repo provides the code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Res

Tuan Manh Lai 24 Oct 24, 2022
Object Detection Projekt in GKI WS2021/22

tfObjectDetection Object Detection Projekt with tensorflow in GKI WS2021/22 Docker Container: docker run -it --name --gpus all -v path/to/project:p

Tim Eggers 1 Jul 18, 2022
Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"

Pytorch Implementation of Deep Orthogonal Fusion of Local and Global Features (DOLG) This is the unofficial PyTorch Implementation of "DOLG: Single-St

DK 96 Jan 06, 2023
ICCV2021 Expert-Goal Trajectory Prediction

ICCV 2021: Where are you heading? Dynamic Trajectory Prediction with Expert Goal Examples This repository contains the code for the paper Where are yo

hz 21 Dec 12, 2022
[CVPR-2021] UnrealPerson: An adaptive pipeline for costless person re-identification

UnrealPerson: An Adaptive Pipeline for Costless Person Re-identification In our paper (arxiv), we propose a novel pipeline, UnrealPerson, that decreas

ZhangTianyu 70 Oct 10, 2022
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Visdom A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Python. Overview Concepts Setup Usage API To

FOSSASIA 9.4k Jan 07, 2023
AI-UPV at IberLEF-2021 EXIST task: Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models

AI-UPV at IberLEF-2021 EXIST task: Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models Descrip

Angel de Paula 1 Jun 08, 2022
The official implementation of paper "Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks" (IJCV under review).

DGMS This is the code of the paper "Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks". Installation Our code works with Pytho

Runpei Dong 3 Aug 28, 2022
Dyalog-apl-docset - Dyalog APL Dash Docset Generator

Dyalog APL Dash Docset Generator o alasa e kili sona kepeken tenpo lili a A Dash

Maciej Goszczycki 1 Jan 10, 2022
The official implementation of the research paper "DAG Amendment for Inverse Control of Parametric Shapes"

DAG Amendment for Inverse Control of Parametric Shapes This repository is the official Blender implementation of the paper "DAG Amendment for Inverse

Elie Michel 157 Dec 26, 2022
Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

pgmpy 2.2k Jan 03, 2023
Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking (CVPR 2021) Pytorch implementation of the ArTIST motion model. In this repo

Fatemeh 38 Dec 12, 2022
Reviving Iterative Training with Mask Guidance for Interactive Segmentation

This repository provides the source code for training and testing state-of-the-art click-based interactive segmentation models with the official PyTorch implementation

Visual Understanding Lab @ Samsung AI Center Moscow 406 Jan 01, 2023
The deployment framework aims to provide a simple, lightweight, fast integrated, pipelined deployment framework that ensures reliability, high concurrency and scalability of services.

savior是一个能够进行快速集成算法模块并支持高性能部署的轻量开发框架。能够帮助将团队进行快速想法验证(PoC),避免重复的去github上找模型然后复现模型;能够帮助团队将功能进行流程拆解,很方便的提高分布式执行效率;能够有效减少代码冗余,减少不必要负担。

Tao Luo 125 Dec 22, 2022
Code of the paper "Multi-Task Meta-Learning Modification with Stochastic Approximation".

Multi-Task Meta-Learning Modification with Stochastic Approximation This repository contains the code for the paper "Multi-Task Meta-Learning Modifica

Andrew 3 Jan 05, 2022
Small utility to demangle Nim symbols in callgrind files

nim_callgrind A small utility to demangle Nim symbols from callgrind files. Usage Run your (Nim) program with something like this: valgrind --tool=cal

kraptor 3 Feb 15, 2022