Skip to main content
SpringerLink
Log in

Suppress and Balance: A Simple Gated Network for Salient Object Detection

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12347))

Included in the following conference series:

Abstract

Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as their basic structures. These methods ignore two key problems when the encoder exchanges information with the decoder: one is the lack of interference control between them, the other is without considering the disparity of the contributions of different encoder blocks. In this work, we propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. We design a novel gated dual branch structure to build the cooperation among different levels of features and improve the discriminability of the whole network. Through the dual branch design, more details of the saliency map can be further restored. In addition, we adopt the atrous spatial pyramid pooling based on the proposed “Fold” operation (Fold-ASPP) to accurately localize salient objects of various scales. Extensive experiments on five challenging datasets demonstrate that the proposed model performs favorably against most state-of-the-art methods under different evaluation metrics.

X. Zhao and Y. Pang—These authors contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)

    Google Scholar 

  2. Amirul Islam, M., Rochan, M., Bruce, N.D., Wang, Y.: Gated feedback refinement network for dense image labeling. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3751–3759 (2017)

    Google Scholar 

  3. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  4. Chen, S., Tan, X., Wang, B., Hu, X.: Reverse attention for salient object detection. In: Proceedings of European Conference on Computer Vision, pp. 234–250 (2018)

    Google Scholar 

  5. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  6. Deng, Z., et al.: R3Net: recurrent residual refinement network for saliency detection. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 684–690 (2018)

    Google Scholar 

  7. Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4548–4557 (2017)

    Google Scholar 

  8. Fang, H., et al.: From captions to visual concepts and back. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1473–1482 (2015)

    Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  10. Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.H.: Deeply supervised salient object detection with short connections. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203–3212 (2017)

    Google Scholar 

  11. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  12. Jiang, Z., Davis, L.S.: Submodular salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2043–2050 (2013)

    Google Scholar 

  13. Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5455–5463 (2015)

    Google Scholar 

  14. Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 478–487 (2016)

    Google Scholar 

  15. Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 280–287 (2014)

    Google Scholar 

  16. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  17. Liu, N., Han, J.: DHSNet: deep hierarchical saliency network for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 678–686 (2016)

    Google Scholar 

  18. Liu, W., Rabinovich, A., Berg, A.C.: ParseNet: looking wider to see better. arXiv preprint arXiv:1506.04579 (2015)

  19. Mahadevan, V., Vasconcelos, N.: Saliency-based discriminant tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  20. Pang, Y., Zhao, X., Zhang, L., Lu, H.: Multi-scale interactive network for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 9413–9422 (2020)

    Google Scholar 

  21. Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: BASNet: boundary-aware salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 7479–7489 (2019)

    Google Scholar 

  22. Ren, Z., Gao, S., Chia, L.T., Tsang, I.W.H.: Region-based saliency detection and its application in object recognition. IEEE Trans. Circuits Syst. Video Technol. 24(5), 769–779 (2013)

    Article  Google Scholar 

  23. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  24. Rui, Z., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  25. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  26. Su, J., Li, J., Zhang, Y., Xia, C., Tian, Y.: Selectivity or invariance: boundary-aware salient object detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3799–3808 (2019)

    Google Scholar 

  27. Wang, L., et al.: Learning to detect salient objects with image-level supervision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 136–145 (2017)

    Google Scholar 

  28. Wang, L., Wang, L., Lu, H., Zhang, P., Ruan, X.: Saliency detection with recurrent fully convolutional networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 825–841. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_50

    Chapter  Google Scholar 

  29. Wang, T., Borji, A., Zhang, L., Zhang, P., Lu, H.: A stagewise refinement model for detecting salient objects in images. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4019–4028 (2017)

    Google Scholar 

  30. Wang, T., Piao, Y., Li, X., Zhang, L., Lu, H.: Deep learning for light field saliency detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 8838–8848 (2019)

    Google Scholar 

  31. Wang, T., et al.: Detect globally, refine locally: a novel approach to saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3127–3135 (2018)

    Google Scholar 

  32. Wang, W., Lai, Q., Fu, H., Shen, J., Ling, H.: Salient object detection in the deep learning era: an in-depth survey. arXiv preprint arXiv:1904.09146 (2019)

  33. Wang, W., Shen, J., Cheng, M.M., Shao, L.: An iterative and cooperative top-down and bottom-up inference network for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5968–5977 (2019)

    Google Scholar 

  34. Wang, W., Zhao, S., Shen, J., Hoi, S.C., Borji, A.: Salient object detection with pyramid attention and salient edges. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1448–1457 (2019)

    Google Scholar 

  35. Wu, R., Feng, M., Guan, W., Wang, D., Lu, H., Ding, E.: A mutual learning method for salient object detection with intertwined multi-supervision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 8150–8159 (2019)

    Google Scholar 

  36. Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3907–3916 (2019)

    Google Scholar 

  37. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)

    Google Scholar 

  38. Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162 (2013)

    Google Scholar 

  39. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)

    Google Scholar 

  40. Yang, G.R., Murray, J.D., Wang, X.J.: A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nat. Commun. 7, 12815 (2016)

    Article  Google Scholar 

  41. Zeng, Y., Zhang, P., Zhang, J., Lin, Z., Lu, H.: Towards high-resolution salient object detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 7234–7243 (2019)

    Google Scholar 

  42. Zhang, L., Dai, J., Lu, H., He, Y., Wang, G.: A bi-directional message passing model for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1741–1750 (2018)

    Google Scholar 

  43. Zhang, L., Zhang, J., Lin, Z., Lu, H., He, Y.: CapSal: leveraging captioning to boost semantics for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 6024–6033 (2019)

    Google Scholar 

  44. Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: Amulet: aggregating multi-level convolutional features for salient object detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 202–211 (2017)

    Google Scholar 

  45. Zhang, X., Wang, T., Qi, J., Lu, H., Wang, G.: Progressive attention guided recurrent network for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 714–722 (2018)

    Google Scholar 

  46. Zhao, T., Wu, X.: Pyramid feature attention network for saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3085–3094 (2019)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China #61876202, #61725202, #61751212 and #61829102, the Dalian Science and Technology Innovation Foundation #2019J12GX039, and the Fundamental Research Funds for the Central Universities # DUT20ZD212.

Author information

Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Xiaoqi Zhao, Youwei Pang, Lihe Zhang & Huchuan Lu

  2. Peng Cheng Laboratory, Shenzhen, China

    Huchuan Lu

  3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Lei Zhang

  4. DAMO Academy, Alibaba Group, Hangzhou, China

    Lei Zhang

Authors
  1. Xiaoqi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Youwei Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lihe Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huchuan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lihe Zhang .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 565 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, X., Pang, Y., Zhang, L., Lu, H., Zhang, L. (2020). Suppress and Balance: A Simple Gated Network for Salient Object Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12347. Springer, Cham. https://doi.org/10.1007/978-3-030-58536-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58536-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58535-8

  • Online ISBN: 978-3-030-58536-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation