论文：链接
代码：https://github.com/shenyunhang/NA-fWebSOD/
关键词： “sole image-level labels on the web” ，“background noise” ， “foreground noise”
为了降低目标检测的成本，本文使用了web image去训练目标检测，因为web image只有image-level的标注，并且带有大量的噪声，所以提出了residual learning structure和bagging-mixup learning去抑制这background和foreground这两种噪声。

Webly Supervised Object Detection (WebSOD)

Introduction

背景：Deep learning network现今取得重大成果，论文中使用web 图片和sole image-level labels去训练网络，但是因为image-level labels on the web 带有大量的噪声, leading to poor performance of the learned detectors.
解决办法：论文中将web image带有的噪声分成了两种：background noise和foreground noise，分布提出了两个措施抑制这两种噪声：
针对background noise：提出了一个residual learning structure，decomposes background noise and models clean data. 分解background noise，同时建模 clean data。同时提出了一个spatially-sensitive entropy criterion去estimate the confidence of background categories being noise。

针对foreground noise：提出了一个 bagging-mixup learning

关于query aeroplane的several web images：
background labels (BL) foreground labels (FL) background noise (BN) foreground noise (FN)
在这个图里面，Background noise指得是person and areoplane都存在图2中，但是person没有被标注，因此是background noise，Foreground noise指得是在image中没有foreground labels ，例如图三中几乎没有areoplane。

Methodology

Contributions:

• 提出了一个residual learning structure，减少noisy label的负面影响通过的分解noise和建模clean data。
• 一个spatially-sensitive entropy 和 a bagging- mixup learning，去评估background labels 的confidence和抑制influence of foreground noise。

**

Noise Decomposition

提出了一个residual feature learning分解background noise 和 建模clean data。
使用multi-task去学习两个head：

1. Weak detection head

input：pooled features
output：proposal features 和 detection scores ,
loss function:

1. Residual detection head

将residual features 和来自于WD的 proposal features 进行相加得到noise feature ：

Spatially-Sensitive Entropy Criterion

Bagging-Mixup Learning

提出一个novel bagging-mixup strategy for data augmentation 去处理 foreground noise的负面影响，
bagging mixup主要包含three steps：

• 首先随机采样同一个class的多个web images

Dirichlet distribution

使用web image训练模型
[1] Santosh K. Divvala, Ali Farhadi, and Carlos Guestrin. Learning everything about anything: Webly-supervised visual concept learning. In CVPR, 2014
[2] Qingyi Tao, Hao Yang, and Jianfei Cai. Zero-Annotation Object Detection with Web Knowledge Transfer. In ECCV, 2018.
[3] Xinlei Chen. Webly Supervised Learning of Convolutional Networks. In ICCV, 2015
[4] Exploiting Web Im- ages for Weakly Supervised Object Detection. TMM,2018

使用image level的label训练模型
[5] Maxime Oquab, L´eon Bottou, Ivan Laptev, and Josef Sivic. Is object localization for free? - Weakly-supervised learning with convolutional neural networks. In CVPR, 2015.
[6] Hakan Bilen and Andrea Vedaldi. Weakly Supervised Deep Detection Networks. In CVPR, 2016
[7] Peng Tang, Xinggang Wang, Xiang Bai, and Wenyu Liu. Multiple Instance Detection Network with Online Instance Classifier Refinement. In CVPR, 2017
[8] Yunchao Wei, Zhiqiang Shen, Bowen Cheng, Honghui Shi, Jinjun Xiong, Jiashi Feng, and Thomas Huang. TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection. In ECCV, 2018.