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A Simple Network with Progressive Structure for Salient Object Detection

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13020))

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Abstract

Recently, most CNNs-based Salient Object Detection (SOD) models have achieved great progress through various feature aggregation strategies. But most of them usually introduce a large number of features into a module for fusion, which results in information dilution. In this paper, we propose a Progressive Fusion Network (PFNet) to solve this problem. The PFNet alleviates the information dilution through a Progressive Fusion Architecture (PFA), which aggregates the features extracted from encoder in a progressive fusion manner. In addition, we use a simple Feature Fusion Module (FFM) that utilize high-level features to enhance the semantic information of low-level features, thereby ensuring the effective fusion of features. Finally, we leverage an Enhanced Loss to guide the optimization process of the network and obtain high-quality saliency maps. The whole network is trained end-to-end without any pre-processing and post-processing. The quantitative and qualitative experimental results on five benchmark datasets demonstrate that the superiority of the proposed approaches.

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References

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

    Google Scholar 

  2. Chen, Z., Qianqian, X., Cong, R., Huang, Q.: Global context-aware progressive aggregation network for salient object detection. Proc. AAAI Conf. Artif. Intell. 34(07), 10599–10606 (2020)

    Google Scholar 

  3. Cong, R., Lei, J., Fu, H., et al.: Review of visual saliency detection with comprehensive information. IEEE Trans. Circuits Syst. Video Technol. 29(10), 2941–2959 (2019)

    Article  Google Scholar 

  4. Fan, D., Gong, C., Cao, Y., Ren, B., Cheng, M.M., Borji, A.: Enhanced-alignment measure for binary foreground Map Evaluation (2018). https://arxiv.org/abs/1805.10421

  5. Feng, M., Lu, H., Ding, E.: Attentive feedback network for boundary-aware salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1623–1632 (2019)

    Google Scholar 

  6. Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2010)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  8. He, S., Lau, R., Liu, W., Yang, Q.: Supercnn: a superpixelwise convolutional neural network for salient object detection. Int. J. Comput. Vision 115(3), 330–344 (2015)

    Article  MathSciNet  Google Scholar 

  9. 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 the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203–3212 (2017)

    Google Scholar 

  10. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  13. Liang, P., Pang, Y., Liao, C., Mei, X., Ling, H.: Adaptive objectness for object tracking. IEEE Signal Process. Lett. 23(7), 949–953 (2016)

    Article  Google Scholar 

  14. Liu, J., Hou, Q., Cheng, M.M., et al.: A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3912–3921 (2019)

    Google Scholar 

  15. Liu, N., Han, J., Yang, M.H.: PiCANet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3089–3098 (2018)

    Google Scholar 

  16. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  17. Mohammadi, S., Noori, M., Bahri, A., Majelan, S.G., Havaei, M.: CAGNet: content-aware guidance for salient object detection. Pattern Recogn. 103, 107303 (2020)

    Article  Google Scholar 

  18. Pang, Y., Zhang, L., Zhao, X., Huchuan, L.: Hierarchical dynamic filtering network for RGB-D salient object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 235–252. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_15

    Chapter  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  21. Ren, Z., Gao, S., Chia, L., et al.: Region-based saliency detection and its application in object recognition. IEEE Trans. Circuits Syst. Video Technol. 24(5), 769–779 (2014)

    Article  Google Scholar 

  22. 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 

  23. Rui, Z., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3586–3593 (2013)

    Google Scholar 

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

  25. Wang, B., Chen, Q., Zhou, M., Zhang, Z., Jin, X., Gai, K.: Progressive feature polishing network for salient object detection. Proc. AAAI Conf. Artif. Intell. 34(07), 12128–12135 (2020)

    Google Scholar 

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

    Google Scholar 

  27. Wang, W., Lai, Q., Fu, H., et al.: Salient object detection in the deep learning era: an in-depth survey (2019). https://arxiv.org/abs/1904.09146

  28. 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 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  32. Zhang, L., Dai, L., Lu, H., et al: A bi-directional message passing model for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1741–1750 (2018)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  35. Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1274 (2015)

    Google Scholar 

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

    Google Scholar 

  37. Zhao, X., Pang, Y., Zhang, L., Huchuan, L., Zhang, L.: Suppress and balance: a simple gated network for salient object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 35–51. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_3

    Chapter  Google Scholar 

  38. Zhou, H., Xie, X., Lai, J., et al.: Interactive two-stream decoder for accurate and fast saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9141–9150 (2020)

    Google Scholar 

  39. Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)

    Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant No. 62076058.

Author information

Authors and Affiliations

  1. Northeastern University, Shenyang, 110819, China

    Boyi Zhou, Gang Yang, Xin Wan, Yutao Wang, Chang Liu & Hangxu Wang

  2. DUT Artificial Intelligence Institute, Dalian, 116024, China

    Boyi Zhou & Xin Wan

Authors
  1. Boyi Zhou
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  2. Gang Yang
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  3. Xin Wan
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  4. Yutao Wang
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  5. Chang Liu
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  6. Hangxu Wang
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Corresponding author

Correspondence to Gang Yang .

Editor information

Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Zhou, B., Yang, G., Wan, X., Wang, Y., Liu, C., Wang, H. (2021). A Simple Network with Progressive Structure for Salient Object Detection. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_33

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  • DOI: https://doi.org/10.1007/978-3-030-88007-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88006-4

  • Online ISBN: 978-3-030-88007-1

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