弱監督的語義分割論文彙總
弱監督語義分割導讀
一般認爲，圖像級的標註是弱標註（例如圖像分類的類別標註），像素級的標註是強標註（例如分割標註的mask標註），對於普通的分割任務來說：
- 數據是圖像，標註是mask，這屬於完全監督問題Supervised；
- 如果標註是annotations或者圖像級標註，這屬於弱監督問題Weakly-supervised；
- 如果標註只有少部分是mask，剩餘是annotations或者圖像級標註，這屬於半監督問題Semi-supervised。
常見的弱標註：bounding box（檢測框），scribbles（塗鴉），points（點），image-level labels（圖像級別）。利用弱標註可以顯著的減少標註時間，如果可以利用弱標籤就可以獲得與mask標籤不相上下的精度和準確率，那我們利用弱標籤和弱監督將會促進產品和數據的迭代。我們按照不同的弱標籤–>語義分割的標籤mask做一下整理 ⬇️。
ps：我們聚焦於語義分割，實例分割和全景分割也有很多成果，並未在本篇博客中列出。
弱監督語義分割論文整理
基於Bounding box的弱監督語義分割
利用深度學習方法和bounding box作爲弱標籤的語義分割研究，之前，基於Bounding box的分割以Grabcut這類算法一枝獨秀，但是效果雖然有突破但不甚理想，所以2016年的Deepcut對於Grabcut做了進一步的優化得到了較好的效果。2015年ICCV中的BoxSup算作是開山之作（He,K爲二作的又一次大佬的實力展現），但其中需要以無監督獲取到的Candidate mask來迭代更新標籤。在這一想法上，發表於2020年CVPR的Self-correcting Networks可以說是將這一想法又往前推進了一步（其實這個工作2018年就已經初見雛形，但2020年才正式發表），將金字塔模型、輔助分割模型和自矯正模塊結合在一起，不過這個網絡裏面有少部分的全監督數據。
基於Bounding Box弱標籤的弱監督語義分割列表：
| 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 |
詳解Paper的博客連接：
【2020 CVPR】Semi-Supervised Semantic Image Segmentation with Self-correcting Networks
基於Image-level labels的弱監督語義分割
基於Image-level labels的弱標籤主要是圖像分類標籤（同理可以擴展到視頻分類中），目前，大多數的基於圖像分類標籤來進行的弱監督語義分割研究的思路：將對圖像分類標籤響應最強烈的區域作爲最初始的種子，通過不同的擴張手段得到更多的區域，從而得到分割的mask。在此過程中，和傳統的機器學習算法結合比較多，因爲需要得到底層特徵的相關性。
基於Image-level labels弱標籤的弱監督語義分割列表：
| 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. |
基於Scribbles的弱監督語義分割
基於Scirbbles弱標籤的弱監督語義分割列表：
| 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.” |
基於Points的弱監督語義分割
基於Points弱標籤的弱監督語義分割列表：
| Year/Meeting | Author/Paper |
|---|---|
| 2016 ECCV | Bearman, A., Russakovsky, O., Ferrari, V., & Fei-Fei, L. What’s the point: Semantic segmentation with point supervision. |
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