Fetching the paper…
Reading the bibliography…
Achieving high-quality semantic segmentation predictions using only image-level labels enables a new level of real-world applicability.
K. P. Fishkin and B. A. Barsky, “An analysis and algorithm for filling propagation,” in Computer-generated images
1985
Earlier work this paper cites.
J. Canny, “A computational approach to edge detection,” IEEE Transactions on pattern analysis and machine intelligence
1986
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International journal of computer vision
2010
Earlier work this paper cites.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2012
Earlier work this paper cites.
Q. Zhu, D. Wu, Y. Xie, and L. Wang, “Quick shift segmentation guided single image haze removal algorithm,” in 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)
2014
Earlier work this paper cites.
K. Khan, M. Mauro, and R. Leonardi, “Multi-class semantic segmentation of faces,” in 2015 IEEE International Conference on Image Processing (ICIP)
2015
Earlier work this paper cites.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
Z. Zhao, S. Voros, Y. Weng, F. Chang, and R. Li, “Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method,” Computer Assisted Surgery
2017
Earlier work this paper cites.
Z.-H. Yuan, T. Lu, Y. Wu, et al
2017
Earlier work this paper cites.
R. Kemker, C. Salvaggio, and C. Kanan, “Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning,” ISPRS journal of photogrammetry and remote sensing
2018
Earlier work this paper cites.
A. Milioto, P. Lottes, and C. Stachniss, “Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns,” in 2018 IEEE international conference on robotics and automation (ICRA)
2018
Cited alongside, same era.
R. Barth, J. IJsselmuiden, J. Hemming, and E. J. Van Henten, “Data synthesis methods for semantic segmentation in agriculture: A capsicum annuum dataset,” Computers and electronics in agriculture
2018
Cited alongside, same era.
J. Ahn and S. Kwak, “Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2018
Cited alongside, same era.
X. Wang, S. You, X. Li, and H. Ma, “Weakly-supervised semantic segmentation by iteratively mining common object features,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2018
Cited alongside, same era.
A. Rehman, S. Naz, M. I. Razzak, F. Akram, and M. Imran, “A deep learning-based framework for automatic brain tumors classification using transfer learning,” Circuits, Systems, and Signal Processing
2020
Later among the works it cites.
Y.-T. Chang, Q. Wang, W.-C. Hung, R. Piramuthu, Y.-H. Tsai, and M.-H. Yang, “Weakly-supervised semantic segmentation via sub-category exploration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2020
Later among the works it cites.
Y. Wang, J. Zhang, M. Kan, S. Shan, and X. Chen, “Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2020
Later among the works it cites.
J. Fan, Z. Zhang, C. Song, and T. Tan, “Learning integral objects with intra-class discriminator for weakly-supervised semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Kanezaki, “Unsupervised image segmentation by backpropagation,” in 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP)
2018
Cited alongside, same era.
T. Meenpal, A. Balakrishnan, and A. Verma, “Facial mask detection using semantic segmentation,” in 2019 4th International Conference on Computing, Communications and Security (ICCCS)
2019
Cited alongside, same era.
P.-T. Jiang, Q. Hou, Y. Cao, M.-M. Cheng, Y. Wei, and H.-K. Xiong, “Integral object mining via online attention accumulation,” in Proceedings of the IEEE/CVF international conference on computer vision
2019
Cited alongside, same era.
J. Ahn, S. Cho, and S. Kwak, “Weakly supervised learning of instance segmentation with inter-pixel relations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
2019
Cited alongside, same era.
J. Lee, E. Kim, S. Lee, J. Lee, and S. Yoon, “Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2019
Cited alongside, same era.
Y. Zeng, Y. Zhuge, H. Lu, and L. Zhang, “Joint learning of saliency detection and weakly supervised semantic segmentation,” in Proceedings of the IEEE/CVF international conference on computer vision
2019
Cited alongside, same era.
F. I. Diakogiannis, F. Waldner, P. Caccetta, and C. Wu, “Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data,” ISPRS Journal of Photogrammetry and Remote Sensing
2020
Cited alongside, same era.
Later among the works it cites.
2020
Later among the works it cites.
J. Ren, H. Gaber, and S. S. Al Jabar, “Applying deep learning to autonomous vehicles: A survey,” in 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD)
2021
Later among the works it cites.
S. Jo and I.-J. Yu, “Puzzle-cam: Improved localization via matching partial and full features,” in 2021 IEEE International Conference on Image Processing (ICIP)
2021
Later among the works it cites.
J. Lee, E. Kim, and S. Yoon, “Anti-adversarially manipulated attributions for weakly and semi-supervised semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2021
Later among the works it cites.
R. Liu and D. He, “Semantic segmentation based on deeplabv3+ and attention mechanism,” in 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)
2021
Later among the works it cites.
J. Xie, X. Hou, K. Ye, and L. Shen, “Clims: Cross language image matching for weakly supervised semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
H. Zhang, C. Wu, Z. Zhang, Y. Zhu, H. Lin, Z. Zhang, Y. Sun, T. He, J. Mueller, R. Manmatha, et al
2022
Later among the works it cites.