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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2020

24.01.2020

Deep Dual-Stream Network with Scale Context Selection Attention Module for Semantic Segmentation

verfasst von: Yifu Liu, Chenfeng Xu, Zhihong Chen, Chao Chen, Han Zhao, Xinyu Jin

Erschienen in: Neural Processing Letters | Ausgabe 3/2020

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Abstract

The fusion of multi-scale features has been an effective method to get state-of-the-art performance in semantic segmentation. In this work, we concentrate on two tricky problems—the intra-class inconsistency and the blur on the localization of object boundaries and tackle them by combining two separate multi-scale context features respectively. Specifically, we propose a dual-stream structure with the scale context selection attention module to enhance the capabilities for multi-scale processing, where one stream collects global-scale context and the other captures local-scale information. Meanwhile, the embedded scale context selection attention module in each stream can adaptively focus on different scale context information to get optimal scale features. Based on our dual-stream structure with attention modules, our network can efficiently make use of multi-scale context to generate more comprehensive and powerful features. Our experiments show that our dual-stream network with scale context selection attention module achieves promising performance on the PASCAL VOC 2012 and PASCAL-Person-Part datasets.
Vorheriger Artikel An Evaluation of RetinaNet on Indoor Object Detection for Blind and Visually Impaired Persons Assistance Navigation
Nächster Artikel Efficient Strategies of Static Features Incorporation into the Recurrent Neural Network

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Fußnoten
Literatur
1.
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
2.
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv e-prints, arXiv:​1409.​0473
3.
Bansal A, Chen X, Russell B, Gupta A, Ramanan D (2017) PixelNet: representation of the pixels, by the pixels, and for the pixels. arXiv e-prints, arXiv:​1702.​06506
4.
Buyssens P, Elmoataz A, Lézoray O (2012) Multiscale convolutional neural networks for vision-based classification of cells. In: Lee KM, Matsushita Y, Rehg JM, Hu Z (eds) Computer vision—ACCV 2012. Springer, Berlin, pp 342–352
5.
Chen L.-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv e-prints, arXiv:​1412.​7062
6.
Chen L.-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2016) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv e-prints, arXiv:​1606.​00915
7.
Chen L.-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv e-prints, arXiv:​1706.​05587
8.
Chen L.-C, Papandreou G, Yuille AL (2013) Learning a dictionary of shape epitomes with applications to image labeling. In: 2013 IEEE international conference on computer vision. IEEE
9.
Chen L.-C, Yang Y, Wang J, Xu W, Yuille AL (2015) Attention to scale: scale-aware semantic image segmentation. arXiv e-prints, arXiv:​1511.​03339
10.
Chen X, Mottaghi R, Liu X, Fidler S, Urtasun R, Yuille A (2014) Detect what you can: detecting and representing objects using holistic models and body parts. arXiv e-prints, arXiv:​1406.​2031
11.
12.
Everingham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136CrossRef
13.
Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915–1929CrossRef
14.
Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2018) Dual attention network for scene segmentation. arXiv e-prints, arXiv:​1809.​02983
15.
Ganin Y, Lempitsky V (2015) N4-fields: neural network nearest neighbor fields for image transforms. In: Cremers D, Reid I, Saito H, Yang M-H (eds) Computer vision—ACCV 2014. Springer, Cham, pp 536–551
16.
Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Rodríguez JG (2017) A review on deep learning techniques applied to semantic segmentation. CoRR, arXiv:​1704.​06857
17.
Ghiasi G, Fowlkes CC (2016) Laplacian pyramid reconstruction and refinement for semantic segmentation. arXiv e-prints, arXiv:​1605.​02264
18.
Hariharan B, Arbelaez P, Bourdev L, Maji S, Malik J (2011) Semantic contours from inverse detectors. In: 2011 international conference on computer vision. IEEE
19.
He C, Hu H (2018) Image captioning with text-based visual attention. Neural Process Lett 49(1):177–185CrossRef
20.
He K, Zhang X, Ren S, Sun J (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. CoRR, arXiv:​1406.​4729
21.
22.
Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670MathSciNetCrossRef
23.
Hong C, Yu J, Zhang J, Jin X, Lee K (2019) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Ind Inf 15(7):3952–3961CrossRef
24.
25.
Kim J, Bukhari W, Lee M (2017) Feature analysis of unsupervised learning for multi-task classification using convolutional neural network. Neural Process Lett 47(3):783–797CrossRef
26.
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Neural Inf Process Syst 25:01
27.
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 2, pp 2169–2178
28.
29.
30.
Liang X, Shen X, Xiang D, Feng J, Lin L, Yan S (2015) Semantic object parsing with local-global long short-term memory. arXiv e-prints, arXiv:​1511.​04510
31.
Lin G, Milan A, Shen C, Reid I (2017) Refinenet: multi-path refinement networks for high-resolution semantic segmentation. In: The IEEE conference on computer vision and pattern recognition (CVPR)
32.
Lin G, Shen C, van dan Hengel A, Reid I (2015) Efficient piecewise training of deep structured models for semantic segmentation. arXiv e-prints, arXiv:​1504.​01013
33.
34.
Liu Z, Li X, Luo P, Change Loy C, Tang X (2015) Semantic image segmentation via deep parsing network. arXiv e-prints, arXiv:​1509.​02634
35.
36.
Neverova N, Wolf C, Taylor GW, Nebout F (2015) Multi-scale deep learning for gesture detection and localization. In: Agapito L, Bronstein MM, Rother C (eds) Computer vision—ECCV 2014 workshops. Springer, Cham, pp 474–490CrossRef
37.
Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: 2015 IEEE international conference on computer vision (ICCV). IEEE
38.
Papandreou G, Chen L.-C, Murphy K, Yuille AL (2015) Weakly- and semi-supervised learning of a DCNN for semantic image segmentation. arXiv e-prints, arXiv:​1502.​02734
39.
Peng C, Zhang X, Yu G, Luo G, Sun J (2017) Large kernel matters—improve semantic segmentation by global convolutional network. In: The IEEE conference on computer vision and pattern recognition (CVPR)
40.
Pohlen T, Hermans A, Mathias M, Leibe B (2017) Full-resolution residual networks for semantic segmentation in street scenes. In: The IEEE conference on computer vision and pattern recognition (CVPR)
41.
Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651CrossRef
42.
Shuai B, Zuo Z, Wang B, Wang G (2018) Scene segmentation with DAG-recurrent neural networks. IEEE Trans Pattern Anal Mach Intell 40(6):1480–1493CrossRef
43.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv e-prints, arXiv:​1706.​03762
44.
Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, Cottrell G (2017) Understanding convolution for semantic segmentation. arXiv e-prints, arXiv:​1702.​08502
45.
46.
47.
Xia F, Wang P, Chen L.-C, Yuille AL (2015) Zoom better to see clearer: human and object parsing with hierarchical auto-zoom net. arXiv e-prints, arXiv:​1511.​06881
48.
Xiao Y, Codevilla F, Gurram A, Urfalioglu O, López AM (2019) Multimodal end-to-end autonomous driving. arXiv e-prints, arXiv:​1906.​03199
49.
Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhutdinov R, Zemel R, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. arXiv e-prints, arXiv:​1502.​03044
50.
Yu C, Wang J, Peng C, Gao C, Yu G, Sang N (2018) Learning a discriminative feature network for semantic segmentation. arXiv e-prints, arXiv:​1804.​09337
51.
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
52.
53.
Yu J, Yang X, Gao F, Tao D (2017) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024CrossRef
54.
Yu J, Zhang B, Kuang Z, Lin D, Fan J (2017) iPrivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans Inf Forensics Secur 12(5):1005–1016CrossRef
55.
56.
Zhang H, Dana K, Shi J, Zhang Z, Wang X, Tyagi A, Agrawal A (2018) Context encoding for semantic segmentation. arXiv e-prints, arXiv:​1803.​08904
57.
Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27(5):2420–2432MathSciNetCrossRef
58.
Zhang W, Hu H, Hu H (2018) Training visual-semantic embedding network for boosting automatic image annotation. Neural Process Lett 48(3):1503–1519CrossRef
59.
60.
Zhao H, Zhang Y, Liu S, Shi J, Loy CC, Lin D, Jia J (2018) Psanet: point-wise spatial attention network for scene parsing. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision—ECCV 2018. Springer, Cham, pp 270–286
61.
Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D, Huang C, Torr PHS (2015) Conditional random fields as recurrent neural networks. arXiv e-prints, arXiv:​1502.​03240
Metadaten
Titel
Deep Dual-Stream Network with Scale Context Selection Attention Module for Semantic Segmentation
verfasst von
Yifu Liu
Chenfeng Xu
Zhihong Chen
Chao Chen
Han Zhao
Xinyu Jin
Publikationsdatum
24.01.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10148-z

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