The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

Related tags

Deep LearningRBN
Overview

Representative Batch Normalization (RBN) with Feature Calibration

The official implementation of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

You only need to replace the BN with our RBN without any other adjustment.

Update

  • 2021.4.9 The Jittor implementation is available now in Jittor.
  • 2021.4.1 The training code of ImageNet classification using RBN is released.

Introduction

Batch Normalization (BatchNorm) has become the default component in modern neural networks to stabilize training. In BatchNorm, centering and scaling operations, along with mean and variance statistics, are utilized for feature standardization over the batch dimension. The batch dependency of BatchNorm enables stable training and better representation of the network, while inevitably ignores the representation differences among instances. We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost. The centering calibration strengthens informative features and reduces noisy features. The scaling calibration restricts the feature intensity to form a more stable feature distribution. Our proposed variant of BatchNorm, namely Representative BatchNorm, can be plugged into existing methods to boost the performance of various tasks such as classification, detection, and segmentation.

Applications

ImageNet classification

The training code of ImageNet classification is released in ImageNet_training folder.

Citation

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

@inproceedings{gao2021rbn,
  title={Representative Batch Normalization with Feature Calibration},
  author={Gao, Shang-Hua and Han, Qi and Li, Duo and Peng, Pai and Cheng, Ming-Ming and Pai Peng},
  booktitle=CVPR,
  year={2021}
}

Contact

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

You might also like...
The official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [paper] Introduction This is the official implementation of ViPNAS: Efficient V

Lumin 42 Sep 26, 2022
[CVPR 2022] Official code for the paper:
[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"

MDCA Calibration This is the official PyTorch implementation for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved

MDCA Calibration 21 Dec 22, 2022
Code for CVPR2021 paper
Code for CVPR2021 paper "Learning Salient Boundary Feature for Anchor-free Temporal Action Localization"

AFSD: Learning Salient Boundary Feature for Anchor-free Temporal Action Localization This is an official implementation in PyTorch of AFSD. Our paper

Tencent YouTu Research 146 Dec 24, 2022
Repo for CVPR2021 paper
Repo for CVPR2021 paper "QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information"

QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information by Masato Tamura, Hiroki Ohashi, and Tomoaki Yosh

null 105 Dec 23, 2022
[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers This is the official PyTorch implementation and models for UP-DETR paper: @a

dddzg 430 Dec 23, 2022
[CVPR2021 Oral] End-to-End Video Instance Segmentation with Transformers
[CVPR2021 Oral] End-to-End Video Instance Segmentation with Transformers

VisTR: End-to-End Video Instance Segmentation with Transformers This is the official implementation of the VisTR paper: Installation We provide instru

Yuqing Wang 687 Jan 7, 2023
[CVPR2021 Oral] FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation.
[CVPR2021 Oral] FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation.

FFB6D This is the official source code for the CVPR2021 Oral work, FFB6D: A Full Flow Biderectional Fusion Network for 6D Pose Estimation. (Arxiv) Tab

Yisheng (Ethan) He 201 Dec 28, 2022
Code of 3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces

3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces Installation After cloning the repo open

null 37 Dec 3, 2022
Official pytorch implementation of "Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization" ACMMM 2021 (Oral)

Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization This is an official implementation of "Feature Stylization and Domain-

null 22 Sep 22, 2022
Comments
  • 关于scaling Calibration的可学习参数b初始化问题

    关于scaling Calibration的可学习参数b初始化问题

    您好，我有个问题想问下，关于scaling calibration中，您对偏置b的参数初始化为1，这是有什么根据吗

    self.scale_weight.data.fill_(0)
self.scale_bias.data.fill_(1)

    因为根据你的公式 image 在限制函数中（沿用你代码的sigmoid函数），你先让可学习参数w初始化为0，那么整个限制函数中一开始就是

    R(wb)

    而wb一开始为1的时候，对应sigmoid的值约为0.731，把他提到方差外部，则方差变为原始方差的0.73*0.73 = 0.5329，相当于方差减半了。若一开始训练就做这么剧烈的变化，是不是对后续训练有一定影响？

    我能理解权重w初始化为0，可以根据centering calibration那一节有

    When the absolute value of wm is close to zero, the centering operation still relies on the running statistics.

    针对这两个可学习参数的初始值设定，有进行过相关实验探讨吗

    opened by MARD1NO 2
  • 使用fuse函数会报错

    使用fuse函数会报错

    def fuse_conv_and_bn(conv, bn): # https://tehnokv.com/posts/fusing-batchnorm-and-conv/ with torch.no_grad(): # init fusedconv = torch.nn.Conv2d(conv.in_channels, conv.out_channels, kernel_size=conv.kernel_size, stride=conv.stride, padding=conv.padding, bias=True)

        # prepare filters
    w_conv = conv.weight.clone().view(conv.out_channels, -1)
    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))

    # prepare spatial bias
    if conv.bias is not None:
        b_conv = conv.bias
    else:
        b_conv = torch.zeros(conv.weight.size(0))
    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

    return fusedconv

    Fusing layers... Traceback (most recent call last): File "test.py", line 263, in opt.augment) File "test.py", line 45, in test model.fuse() File "/home/zzf/Desktop/yolov3-dbb+representbatchnorm/models.py", line 402, in fuse fused = torch_utils.fuse_conv_and_bn(conv, b) File "/home/zzf/Desktop/yolov3-dbb+representbatchnorm/utils/torch_utils.py", line 83, in fuse_conv_and_bn w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) RuntimeError: matrix or a vector expected

    把自己网络的batchnorm 改变后会报错麻烦解决以下。

    opened by xiaowanzizz 1
  • 论文中的一些疑惑

    论文中的一些疑惑

    您好，感谢您的工作！论文里的一些地方我没有明白，希望您能解答一下，谢谢。 ① image When the Km in Eqn.(5) is set to Uc，the running mean of Km is equal to E(X) 请问这句话应该怎么理解呢？ ②在Choice of Instance Statistics中，你提到的the mean and standard division over spatial dimensions, denoted by image 请问这两个值具体怎么计算？ ③ ”Since scaling calibration only restricts the feature intensity while not changing the amount of information, scaling with both channel and spatial statistics results in a similar performance.”，请问改变信息的数量是什么意思呢？

    opened by songyonger 1
Releases(pretrained)
Owner
Open source projects of ShangHua-Gao
Open source projects of ShangHua-Gao
GitHub Repository
PyTorch wrappers for using your model in audacity!

audacitorch This package contains utilities for prepping PyTorch audio models for use in Audacity. More specifically, it provides abstract classes for

Hugo Flores García 130 Dec 14, 2022
Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022)

Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022) Please cite "Independent SE(3)-Equivar

Octavian Ganea 154 Jan 02, 2023
[ICCV 2021] Official Tensorflow Implementation for "Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions"

KPAC: Kernel-Sharing Parallel Atrous Convolutional block This repository contains the official Tensorflow implementation of the following paper: Singl

Hyeongseok Son 50 Dec 29, 2022
Car Parking Tracker Using OpenCv

Car Parking Vacancy Tracker Using OpenCv I used basic image processing methods i

Adwait Kelkar 30 Dec 03, 2022
An Open-Source Package for Information Retrieval.

OpenMatch An Open-Source Package for Information Retrieval. 😃 What's New Top Spot on TREC-COVID Challenge (May 2020, Round2) The twin goals of the ch

THUNLP 439 Dec 27, 2022
A module that used for encrypt code which includes RSA and AES

软件加密模块 requirement： Crypto,pycryptodome,pyqt5 本地加密信息为随机字符串 使用说明 命令行参数 -h 帮助 -checkWorking 检查是否能正常工作，后接1确认指令 -checkEndDate 检查截至日期，后接1确认指令 -activateCode

2 Sep 27, 2022
REGTR: End-to-end Point Cloud Correspondences with Transformers

REGTR: End-to-end Point Cloud Correspondences with Transformers This repository contains the source code for REGTR. REGTR utilizes multiple transforme

Zi Jian Yew 108 Dec 17, 2022
Score refinement for confidence-based 3D multi-object tracking

Score refinement for confidence-based 3D multi-object tracking Our video gives a brief explanation of our Method. This is the official code for the pa

Cognitive Systems Research Group 47 Dec 26, 2022
This repository contains a pytorch implementation of "HeadNeRF: A Real-time NeRF-based Parametric Head Model (CVPR 2022)".

HeadNeRF: A Real-time NeRF-based Parametric Head Model This repository contains a pytorch implementation of "HeadNeRF: A Real-time NeRF-based Parametr

294 Jan 01, 2023
ICON: Implicit Clothed humans Obtained from Normals

ICON: Implicit Clothed humans Obtained from Normals arXiv, December 2021. Yuliang Xiu · Jinlong Yang · Dimitrios Tzionas · Michael J. Black Table of C

Yuliang Xiu 1.1k Dec 30, 2022
Distributionally robust neural networks for group shifts

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization This code implements the g

151 Dec 25, 2022
This's an implementation of deepmind Visual Interaction Networks paper using pytorch

Visual-Interaction-Networks An implementation of Deepmind visual interaction networks in Pytorch. Introduction For the purpose of understanding the ch

Mahmoud Gamal Salem 166 Dec 06, 2022
This is the official PyTorch implementation of our paper: "Artistic Style Transfer with Internal-external Learning and Contrastive Learning".

Artistic Style Transfer with Internal-external Learning and Contrastive Learning This is the official PyTorch implementation of our paper: "Artistic S

51 Dec 20, 2022
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Trieu 6.1k Dec 30, 2022
Tensorflow implementation of Character-Aware Neural Language Models.

Character-Aware Neural Language Models Tensorflow implementation of Character-Aware Neural Language Models. The original code of author can be found h

Taehoon Kim 751 Dec 26, 2022
Code for paper entitled "Improving Novelty Detection using the Reconstructions of Nearest Neighbours"

NLN: Nearest-Latent-Neighbours A repository containing the implementation of the paper entitled Improving Novelty Detection using the Reconstructions

Michael (Misha) Mesarcik 4 Dec 14, 2022
HyperPose is a library for building high-performance custom pose estimation applications.

HyperPose is a library for building high-performance custom pose estimation applications.

TensorLayer Community 1.2k Jan 04, 2023
The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible

pyrelational is a python active learning library developed by Relation Therapeutics for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximat

Relation Therapeutics 95 Dec 27, 2022
DataCLUE: 国内首个以数据为中心的AI测评（含模型分析报告）

DataCLUE: A Benchmark Suite for Data-centric NLP You can get the english version of README. 以数据为中心的AI测评(DataCLUE) 内容导引 章节 描述 简介 介绍以数据为中心的AI测评(DataCLUE

CLUE benchmark 135 Dec 22, 2022
MazeRL is an application oriented Deep Reinforcement Learning (RL) framework

MazeRL is an application oriented Deep Reinforcement Learning (RL) framework, addressing real-world decision problems. Our vision is to cover the complete development life cycle of RL applications ra

EnliteAI GmbH 222 Dec 24, 2022