弱监督的语义分割论文汇总

弱监督的语义分割论文汇总
弱监督语义分割导读
弱监督语义分割论文整理
基于Bounding box的弱监督语义分割
基于Image-level labels的弱监督语义分割
基于Scribbles的弱监督语义分割
基于Points的弱监督语义分割
弱监督语义分割导读
一般认为，图像级的标注是弱标注（例如图像分类的类别标注），像素级的标注是强标注（例如分割标注的mask标注），对于普通的分割任务来说：

数据是图像，标注是mask，这属于完全监督问题Supervised；
如果标注是annotations或者图像级标注，这属于弱监督问题Weakly-supervised；
如果标注只有少部分是mask，剩余是annotations或者图像级标注，这属于半监督问题Semi-supervised。
在这里插入图片描述
常见的弱标注：bounding box（检测框），scribbles（涂鸦），points（点），image-level labels（图像级别）。利用弱标注可以显著的减少标注时间，如果可以利用弱标签就可以获得与mask标签不相上下的精度和准确率，那我们利用弱标签和弱监督将会促进产品和数据的迭代。我们按照不同的弱标签–>语义分割的标签mask做一下整理 ⬇️。
ps：我们聚焦于语义分割，实例分割和全景分割也有很多成果，并未在本篇博客中列出。

弱监督语义分割论文整理
基于Bounding box的弱监督语义分割
利用深度学习方法和bounding box作为弱标签的语义分割研究，之前，基于Bounding box的分割以Grabcut这类算法一枝独秀，但是效果虽然有突破但不甚理想，所以2016年的Deepcut对于Grabcut做了进一步的优化得到了较好的效果。2015年ICCV中的BoxSup算作是开山之作（He,K为二作的又一次大佬的实力展现），但其中需要以无监督获取到的Candidate mask来迭代更新标签。在这一想法上，发表于2020年CVPR的Self-correcting Networks可以说是将这一想法又往前推进了一步（其实这个工作2018年就已经初见雏形，但2020年才正式发表），将金字塔模型、辅助分割模型和自矫正模块结合在一起，不过这个网络里面有少部分的全监督数据。

基于Bounding Box弱标签的弱监督语义分割列表：

Year/Meeting Author/Paper
2015 ICCV Dai, J., He, K., & Sun, J. BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
2015 ICCV Papandreou, G., Chen, L.-C., Murphy, K., & Yuille, A. L. Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
2016 Axiv Rajchl, Martin, et al. DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
2017 CVPR Khoreva, A., Benenson, R., Hosang, J., Hein, M., & Schiele, B. Simple Does It: Weakly Supervised Instance and Semantic Segmentation
2020 CVPR Mostafa S. Ibrahim, Arash Vahdat, & William G. Macready. Weakly Supervised Semantic Image Segmentation with Self-correcting Networks
详解Paper的博客连接：
【2020 CVPR】Semi-Supervised Semantic Image Segmentation with Self-correcting Networks

基于Image-level labels的弱监督语义分割
基于Image-level labels的弱标签主要是图像分类标签（同理可以扩展到视频分类中），目前，大多数的基于图像分类标签来进行的弱监督语义分割研究的思路：将对图像分类标签响应最强烈的区域作为最初始的种子，通过不同的扩张手段得到更多的区域，从而得到分割的mask。在此过程中，和传统的机器学习算法结合比较多，因为需要得到底层特征的相关性。

基于Image-level labels弱标签的弱监督语义分割列表：

Year/Meeting Author/Paper
2014 arXiv Pathak, D., Shelhamer, E., Long, J., & Darrell, T. Fully Convolutional Multi-Class Multiple Instance Learning.
2015 CVPR Pinheiro, P. O., Collobert, R., & Epfl, D. L. From Image-level to Pixel-level Labeling with Convolutional Networks
2015 ICCV Papandreou, G., Chen, L.-C., Murphy, K., & Yuille, A. L. Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation.
2016 Pattern Recognition Wei, Y., Liang, X., Chen, Y., Jie, Z., Xiao, Y., Zhao, Y., & Yan, S. Learning to segment with image-level annotations.
2016 ECCV Shimoda, W., & B, K. Y. Distinct Class-Specific Saliency Maps for Weakly Supervised Semantic Segmentation.
2016 ECCV Saleh, F., Akbarian, M. S. A., Salzmann, M., Petersson, L., Gould, S., & Alvarez, J. M. Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation.
2016 ECCV Kolesnikov, A., & Lampert, C. H. Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation.
2016 ECCV Qi, X., Liu, Z., Shi, J., Zhao, H., & Jia, J. Augmented feedback in semantic segmentation under image level supervision.
2017 PAMI Wei, Y., Liang, X., Chen, Y., Shen, X., Cheng, M.-M., Zhao, Y., & Yan, S. STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation.
2017 CVPR Roy, A., & Todorovic, S. Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation.
2017 CVPR Durand, T., Mordan, T., Thome, N., & Cord, M. WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation.
2017 CVPR Wei, Y., Feng, J., Liang, X., Cheng, M.-M., Zhao, Y., & Yan, S. Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach.
2017 AAAI Hong, S., Yeo, D., Kwak, S., Lee, H., & Han, B. Weakly Supervised Semantic Segmentation using Web-Crawled Videos.
2018 CVPR Wang, X., You, S., Li, X., & Ma, H. Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features.
2018 CVPR Huang, Z., Wang, X., Wang, J., Liu, W., & Wang, J. Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing.
2018 CVPR Ahn, J., & Kwak, S. Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation.
2018 CVPR Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., & Huang, T. S. Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
2019 CVPR Shen Y, Ji R, Wang Y, et al. Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation
2019 ICCV Shimoda W, Yanai K. Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation
2019 ICCV Zeng Y, Zhuge Y, Lu H, et al. Joint learning of saliency detection and weakly supervised semantic segmentation
2020 Neurocomputing Wang X, Ma H, You S. Deep clustering for weakly-supervised semantic segmentation in autonomous driving scenes.
2020 IJCV Wang X, Liu S, Ma H, Yang M-H. Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning.
基于Scribbles的弱监督语义分割
基于Scirbbles弱标签的弱监督语义分割列表：

Year/Meeting Author/Paper
2016 CVPR Lin, D., Dai, J., Jia, J., He, K., & Sun, J. ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation.
2016 MICCAI Çiçek, Özgün, et al. “3d u-net: learning dense volumetric segmentation from sparse annotation.”
**

• 基于Points的弱监督语义分割

**
基于Points弱标签的弱监督语义分割列表：

Year/Meeting Author/Paper
2016 ECCV Bearman, A., Russakovsky, O., Ferrari, V., & Fei-Fei, L. What’s the point: Semantic segmentation with point supervision.