2018

OCNet: Object Context Network for Scene Parsing

Yuan, Yuhui, Huang, Lang, Guo, Jianyuan et al.

Understand

In this paper, we address the semantic segmentation task with a new context aggregation scheme named \emph{object context}, which focuses on enhancing the role of object information.

Motivated by the fact that the category of each pixel is inherited from the object it belongs to, we define the object context for each pixel as the set of pixels that belong to the same category as the given pixel in the image.
We use a binary relation matrix to represent the relationship between all pixels, where the value one indicates the two selected pixels belong to the same category and zero otherwise.
We propose to use a dense relation matrix to serve as a surrogate for the binary relation matrix.

Built on

Greenspun P (1999) Philip and Alex’s guide to Web publishing. Morgan Kaufmann
1999
Earlier work this paper cites.
Roelofs G, Koman R (1999) PNG: the definitive guide. O’Reilly & Associates, Inc
1999
Earlier work this paper cites.
Divvala SK, Hoiem D, Hays JH, Efros AA, Hebert M (2009) An empirical study of context in object detection. In: CVPR
2009
Earlier work this paper cites.
Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: ECCV
2014
Earlier work this paper cites.
Mottaghi R, Chen X, Liu X, Cho NG, Lee SW, Fidler S, Urtasun R, Yuille A (2014) The role of context for object detection and semantic segmentation in the wild. In: CVPR
2014
Earlier work this paper cites.
Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:150203167
2015
Earlier work this paper cites.
Liu W, Rabinovich A, Berg AC (2015) Parsenet: Looking wider to see better. arXiv:150604579
2015
Earlier work this paper cites.
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: CVPR
2015
Earlier work this paper cites.
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: MICCAI
2015
Earlier work this paper cites.
Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: CVPR
2016
Earlier work this paper cites.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR
2016
Earlier work this paper cites.
Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convolutions. In: ICLR
2016
Earlier work this paper cites.
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. PAMI
2017
Earlier work this paper cites.
Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:170605587
2017
Earlier work this paper cites.
Gong K, Liang X, Zhang D, Shen X, Lin L (2017) Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing. In: CVPR
2017
Earlier work this paper cites.
He K, Gkioxari G, Dollár P, Girshick RB (2017) Mask R-CNN. In: ICCV
2017
Earlier work this paper cites.
Krishna R, Zhu Y, Groth O, Johnson J, Hata K, Kravitz J, Chen S, Kalantidis Y, Li LJ, Shamma DA, et al. (2017) Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV
2017
Earlier work this paper cites.
Shuai B, Zuo Z, Wang B, Wang G (2017) Scene segmentation with dag-recurrent neural networks. PAMI
2017
Earlier work this paper cites.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: NIPS
2017
Earlier work this paper cites.

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Xie G, Wang J, Zhang T, Lai J, Hong R, Qi GJ (2018) Igcv
2018
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Yang M, Yu K, Zhang C, Li Z, Yang K (2018) Denseaspp for semantic segmentation in street scenes. In: CVPR
2018
Closest in time.
Yue K, Sun M, Yuan Y, Zhou F, Ding E, Xu F (2018) Compact generalized non-local network. In: NIPS
2018
Closest in time.
Zhang H, Dana K, Shi J, Zhang Z, Wang X, Tyagi A, Agrawal A (2018) Context encoding for semantic segmentation. In: CVPR
2018
Closest in time.
Zhao H, Yi Z, Shu L, Jianping S, Loy CC, Dahua L, Jia J (2018) Psanet: Point-wise spatial attention network for scene parsing. In: ECCV
2018
Closest in time.
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: MICCAI
2018
Closest in time.
Cheng B, Chen LC, Wei Y, Zhu Y, Huang Z, Xiong J, Huang TS, Hwu WM, Shi H (2019) Spgnet: Semantic prediction guidance for scene parsing. In: Proceedings of the IEEE International Conference on Computer Vision, pp 5218–5228
2019
Closest in time.
Child R, Gray S, Radford A, Sutskever I (2019) Generating long sequences with sparse transformers. arXiv:190410509
2019
Closest in time.
He J, Deng Z, Zhou L, Wang Y, Qiao Y (2019) Adaptive pyramid context network for semantic segmentation. In: CVPR
2019
Closest in time.
Hoyer L, Munoz M, Katiyar P, Khoreva A, Fischer V (2019) Grid saliency for context explanations of semantic segmentation. In: NIPS
2019
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Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: ICCV
2019
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Kuo W, Angelova A, Malik J, Lin TY (2019) Shapemask: Learning to segment novel objects by refining shape priors. In: ICCV
2019
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Li X, Zhong Z, Wu J, Yang Y, Lin Z, Liu H (2019) Expectation-maximization attention networks for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 9167–9176
2019
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Pang Y, Li Y, Shen J, Shao L (2019) Towards bridging semantic gap to improve semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4230–4239
2019
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Shetty R, Schiele B, Fritz M (2019) Not using the car to see the sidewalk–quantifying and controlling the effects of context in classification and segmentation. In: CVPR
2019
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Tian Z, He T, Shen C, Yan Y (2019) Decoders matter for semantic segmentation: Data-dependent decoding enables flexible feature aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3126–3135
2019
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Wang W, Zhang Z, Qi S, Shen J, Pang Y, Shao L (2019) Learning compositional neural information fusion for human parsing. In: Proceedings of the IEEE International Conference on Computer Vision, pp 5703–5713
2019
Closest in time.
Yuan Y, Chen X, Wang J (2019) Object-contextual representations for semantic segmentation. arXiv preprint arXiv:190911065
2019
Closest in time.

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