Top

Neural Processing Letters

Published in:

05-09-2021

Refine for Semantic Segmentation Based on Parallel Convolutional Network with Attention Model

Authors: Gang Peng, Shiqi Yang, Hao Wang

Published in: Neural Processing Letters | Issue 6/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

High-precision semantic segmentation methods require global information and more detailed local features. It is difficult for ordinary convolutional neural networks to efficiently use this information. In response to the above issues, this paper uses the attention to scale method and proposes a novel attention model for semantic segmentation, which aggregates multi-scale and context features to refine prediction. Specifically, the skeleton convolutional neural network framework takes in multiple different scales inputs, by which means the CNN can get representations in different scales. The proposed attention model will handle the features from different scale streams respectively and integrate them. Then location attention branch of the model learns to softly weight the multi-scale features at each pixel location. Moreover, we add an recalibrating branch, parallel to where location attention comes out, to recalibrate the score map per class. We achieve quite competitive results on PASCAL VOC 2012 and ADE20K datasets, which surpass baseline and related works.

previous article Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network

next article GAN Based Three-Stage-Training Algorithm for Multi-view Facial Expression Recognition

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Wang F, Jiang M, Qian C, et al. (2017) Residual attention network for image classification[J]. arXiv preprint https://arxiv.org/abs/1704.06904

Zheng H, Fu J, Mei T, et al. (2017) Learning multi-attention convolutional neural network for fine-grained image recognition[C]. In Int. Conf. on Computer Vision. Venice, Italy, 6

Sun M, Yuan Y, Zhou F, et al. (2018) Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition[J]. arXiv preprint https://arxiv.org/abs/1806.05372

Chen X, Xu C, Yang X, et al. (2018) Attention-GAN for Object Transfiguration in Wild Images[J]. arXiv preprint https://arxiv.org/abs/1803.06798

Chen, Liang-Chieh, et al. (2016) "Attention to scale: Scale-aware semantic image segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition

Zhang, Hang, et al. (2018) "Context encoding for semantic segmentation." IEEE conference on Computer Vision and Pattern Recognition. June 18–23, 2018, Salt Lake City, USA

Li, Hanchao, et al. (2018) "Pyramid attention network for semantic segmentation." arXiv preprint https://arxiv.org/abs/1805.10180

Fu, Jun, et al. (2019) "Dual attention network for scene segmentation." IEEE Conference on Computer Vision and Pattern Recognition. June 15–20, 2019, Long Beach, USA

Liu, Yifu, et al. (2020) "Deep Dual-Stream Network with Scale Context Selection Attention Module for Semantic Segmentation." Neural Processing Letters: 1–19

10.

Long, J., Shelhamer, E., Darrell, T. (2015): Fully convolutional networks for semantic segmentation. IEEE conference on computer vision and pattern recognition, Proceedings:3431–3440, June 7–12, 2015, Boston, USA.

11.

Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495CrossRef

12.

Noh, H., Hong, S., Han, B. (2015): Learning deconvolution network for semantic segmentation. IEEE International Conference on Computer Vision, Proceedings: 1520–1528, June 7–12 2015, Boston, USA

13.

Chen, Liang-Chieh, et al. (2017) "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs." IEEE transactions on pattern analysis and machine intelligence 40.4 (2017): 834–848

14.

Yu, F., Koltun, V. (2015): Multi-scale context aggregation by dilated convolutions. arXiv preprint https://arxiv.org/abs/1511.07122

15.

Liu, W., Rabinovich, A., Berg, A.C. (2015): Parsenet: Looking wider to see better. arXiv preprint https://arxiv.org/abs/1506.04579

16.

Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. IEEE Conference on Computer Vision and Pattern Recognition, Proceedings:2881–2890, July 21–26, 2017, Honolulu, USA

17.

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio (2014) "Neural machine translation by jointly learning to align and translate." arXiv preprint https://arxiv.org/abs/1409.0473

18.

Chen, Jingyuan, et al. (2017) "Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention." International ACM SIGIR conference on Research and Development in Information Retrieval. Aug 7–11, Shinjuku, Japan

19.

Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., Tang, X. (2017): Residual attention network for image classification. IEEE Conference on Computer Vision and Pattern Recognition, Proceedings: 3156–3164, July 21–26, Honolulu, USA

20.

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y. (2015): Show, attend and tell: Neural image caption generation with visual attention. International Conference on Machine Learning, Proceedings: 2048–2057, July 6–11, Lille, France,

21.

Song, X., Feng, F., Han, X., Yang, X., Liu, W., Nie, L. (2018): Neural compatibility modeling with attentive knowledge distillation. arXiv preprint https://arxiv.org/abs/1805.00313

22.

Hariharan, B., Arbel´aez, P., Girshick, R., Malik, J. (2015): Hypercolumns for object segmentation and fine-grained localization. IEEE conference on computer vision and pattern recognition, Proceedings:447–456, June 7–12, Boston, USA

23.

Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S. (2018): Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition, Proceedings: 7268–7277, June 18–23, Salt Lake City, USA

24.

Li, X., Jie, Z., Wang, W., Liu, C., Yang, J., Shen, X., Lin, Z., Chen, Q., Yan, S., Feng, J.: Foveanet (2017): Perspective-aware urban scene parsing. IEEE International Conference on Computer Vision, Proceedings:784–792, Oct 22–29, 2017, Venice, Italy

25.

Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascalnetwork.org/challenges/VOC/voc2012/workshop/index.html

26.

Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A. (2016): Semantic understanding of scenes through the ade20k dataset. arXiv preprint https://arxiv.org/abs/1608.05442

27.

Hariharan, B., Arbel´aez, P., Bourdev, L., Maji, S., Malik, J. (2011): Semantic contours from inverse detectors. Computer Vision (ICCV), 2011 IEEE International Conference on, Proceedings:991–998, Nov 6–13, Barcelona, Spain

28.

Chen, Liang-Chieh, et al. (2014)"Semantic image segmentation with deep convolutional nets and fully connected crfs." arXiv preprint https://arxiv.org/abs/1412.7062

29.

Simonyan, K., Zisserman, A. (2014): Very deep convolutional networks for large-scale image recognition. arXiv preprint https://arxiv.org/abs/1409.1556

30.

Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032MathSciNetCrossRef

31.

Yu, J., Tan, M., Zhang, H., Tao, D., & Rui, Y. (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE transactions on pattern analysis and machine intelligence

Title: Refine for Semantic Segmentation Based on Parallel Convolutional Network with Attention Model
Authors: Gang Peng
Shiqi Yang
Hao Wang
Publication date: 05-09-2021
Publisher: Springer US
Published in: Neural Processing Letters / Issue 6/2021
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-021-10587-7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 6/2021

CCCIH: Content-consistency Coverless Information Hiding Method Based on Generative Models

Multi-stage Genetic Algorithm and Deep Neural Network for Robot Execution Failure Detection

Correlation Projection for Analytic Learning of a Classification Network

Residual Spatiotemporal Autoencoder with Skip Connected and Memory Guided Network for Detecting Video Anomalies

Neural Network for a Novel Disturbance Optimal Control Model for Inventory and Production Planning in a Four-Echelon Supply Chain with Reverse Logistic

New Results on Global Exponential Stability of Genetic Regulatory Networks with Diffusion Effect and Time-Varying Hybrid Delays