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In this work, we explore data augmentations for knowledge distillation on semantic segmentation.
Fasterseg: Searching for faster real-time semantic segmentation
Chen, W.; Gong, X.; Liu, X.; Zhang, Q.; Li, Y.; and Wang, Z. 2019 · 1912
Earlier work this paper cites.
A comprehensive overhaul of feature distillation
Heo, B.; Kim, J.; Yun, S.; Park, H.; Kwak, N.; and Choi, J. Y. 2019 · 1930
Earlier work this paper cites.
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Lin, G.; Milan, A.; Shen, C.; and Reid, I. 2017 · 1934
Earlier work this paper cites.
The Pascal Visual Object Classes (VOC) Challenge
Everingham, M.; Van Gool, L.; Williams, C. K. I.; Winn, J.; and Zisserman, A. 2010 · 2010
Earlier work this paper cites.
Better mixing via deep representations
Bengio, Y.; Mesnil, G.; Dauphin, Y.; and Rifai, S. 2013 · 2013
Earlier work this paper cites.
The Role of Context for Object Detection and Semantic Segmentation in the Wild
Mottaghi, R.; Chen, X.; Liu, X.; Cho, N.-G.; Lee, S.-W.; Fidler, S.; Urtasun, R.; and Yuille, A. 2014 · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Romero, A.; Ballas, N.; Kahou, S. E.; Chassang, A.; Gatta, C.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Bayesian convolutional neural networks with Bernoulli approximate variational inference
Gal, Y.; and Ghahramani, Z. 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J.; Shelhamer, E.; and Darrell, T. 2015 · 2015
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
Yu, F.; and Koltun, V. 2015 · 2015
Earlier work this paper cites.
Semantic segmentation with boundary neural fields
Bertasius, G.; Shi, J.; and Torresani, L. 2016 · 2016
Earlier work this paper cites.
Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform
Chen, L.-C.; Barron, J. T.; Papandreou, G.; Murphy, K.; and Yuille, A. L. 2016 · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Convolutional network for attribute-driven and identity-preserving human face generation
Li, M.; Zuo, W.; and Zhang, D. 2016 · 2016
Earlier work this paper cites.
Enet A deep neural network architecture for real-time semantic segmentation
Paszke, A.; Chaurasia, A.; Kim, S.; and Culurciello, E. 2016 · 2016
Earlier work this paper cites.
Zagoruyko, S.; and Komodakis, N. 2016 · 2016
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2017 · 2017
Earlier work this paper cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A.; and Gal, Y. 2017 · 2017
Cited alongside, same era.
Large kernel matters–improve semantic segmentation by global convolutional network
Peng, C.; Zhang, X.; Yu, G.; Luo, G.; and Sun, J. 2017 · 2017
Cited alongside, same era.
Deep feature interpolation for image content changes
Upchurch, P.; Gardner, J.; Pleiss, G.; Pless, R.; Snavely, N.; Bala, K.; and Weinberger, K. 2017 · 2017
Cited alongside, same era.
Pyramid scene parsing network
Zhao, H.; Shi, J.; Qi, X.; Wang, X.; and Jia, J. 2017 · 2017
Cited alongside, same era.
Scene parsing through ade20k dataset
Zhou, B.; Zhao, H.; Puig, X.; Fidler, S.; Barriuso, A.; and Torralba, A. 2017 · 2017
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.-C.; Zhu, Y.; Papandreou, G.; Schroff, F.; and Adam, H. 2018 · 2018
Gated-scnn: Gated shape cnns for semantic segmentation
Takikawa, T.; Acuna, D.; Jampani, V.; and Fidler, S. 2019 · 2019
Later among the works it cites.
Implicit semantic data augmentation for deep networks
Wang, Y.; Pan, X.; Song, S.; Zhang, H.; Huang, G.; and Wu, C. 2019 · 2019
Later among the works it cites.
Context-reinforced semantic segmentation
Zhou, Y.; Sun, X.; Zha, Z.-J.; and Zeng, W. 2019 · 2019
Later among the works it cites.
MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark
Contributors, M. 2020 · 2020
Later among the works it cites.
Inter-region affinity distillation for road marking segmentation
Hou, Y.; Ma, Z.; Liu, C.; Hui, T.-W.; and Loy, C. C. 2020 · 2020
Later among the works it cites.
Structured knowledge distillation for dense prediction
Liu, Y.; Shu, C.; Wang, J.; and Shen, C. 2020 · 2020
Later among the works it cites.
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Cited alongside, same era.
Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation
Mehta, S.; Rastegari, M.; Caspi, A.; Shapiro, L.; and Hajishirzi, H. 2018 · 2018
Cited alongside, same era.
Non-local neural networks
Wang, X.; Girshick, R.; Gupta, A.; and He, K. 2018 · 2018
Cited alongside, same era.
Denseaspp for semantic segmentation in street scenes
Yang, M.; Yu, K.; Zhang, C.; Li, Z.; and Yang, K. 2018 · 2018
Cited alongside, same era.
Ocnet: Object context network for scene parsing
Yuan, Y.; Huang, L.; Guo, J.; Zhang, C.; Chen, X.; and Wang, J. 2018 · 2018
Cited alongside, same era.
Context encoding for semantic segmentation
Zhang, H.; Dana, K.; Shi, J.; Zhang, Z.; Wang, X.; Tyagi, A.; and Agrawal, A. 2018 · 2018
Cited alongside, same era.
Boundary-aware feature propagation for scene segmentation
Ding, H.; Jiang, X.; Liu, A. Q.; Thalmann, N. M.; and Wang, G. 2019 · 2019
Cited alongside, same era.
Intra-class feature variation distillation for semantic segmentation
Wang, Y.; Zhou, W.; Jiang, T.; Bai, X.; and Xu, Y. 2020 · 2020
Later among the works it cites.
Context prior for scene segmentation
Yu, C.; Wang, J.; Gao, C.; Yu, G.; Shen, C.; and Sang, N. 2020 · 2020
Later among the works it cites.
Object-contextual representations for semantic segmentation
Yuan, Y.; Chen, X.; and Wang, J. 2020 · 2020
Later among the works it cites.
Joint semantic segmentation and boundary detection using iterative pyramid contexts
Zhen, M.; Wang, J.; Zhou, L.; Li, S.; Shen, T.; Shang, J.; Fang, T.; and Quan, L. 2020 · 2020
Later among the works it cites.
Squeeze-and-attention networks for semantic segmentation
Zhong, Z.; Lin, Z. Q.; Bidart, R.; Hu, X.; Daya, I. B.; Li, Z.; Zheng, W.-S.; Li, J.; and Wong, A. 2020 · 2020
Later among the works it cites.
Channel-Wise Knowledge Distillation for Dense Prediction
Shu, C.; Liu, Y.; Gao, J.; Yan, Z.; and Shen, C. 2021 · 2021
Later among the works it cites.
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Xie, E.; Wang, W.; Yu, Z.; Anandkumar, A.; Alvarez, J. M.; and Luo, P. 2021 · 2021
Later among the works it cites.
Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation
Yu, C.; Gao, C.; Wang, J.; Yu, G.; Shen, C.; and Sang, N. 2021 · 2021
Later among the works it cites.
Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Zheng, S.; Lu, J.; Zhao, H.; Zhu, X.; Luo, Z.; Wang, Y.; Fu, Y.; Feng, J.; Xiang, T.; Torr, P. H.; et al. 2021 · 2021
Later among the works it cites.
Rethinking soft labels for knowledge distillation: A bias-variance tradeoff perspective
Zhou, H.; Song, L.; Chen, J.; Zhou, Y.; Wang, G.; Yuan, J.; and Zhang, Q. 2021 · 2021
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Improved Knowledge Distillation via Full Kernel Matrix Transfer
Qian, Q.; Li, H.; and Hu, J. 2022 · 2022
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Cross-image relational knowledge distillation for semantic segmentation
Yang, C.; Zhou, H.; An, Z.; Jiang, X.; Xu, Y.; and Zhang, Q. 2022 · 2022
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