Abstract
The deep learning method has achieved great success in saliency prediction task. Like depth and depth, the attention mechanism has been proved to be effective in enhancing the performance of Convolutional Neural Network (CNNs) in many studies. In this paper, we propose a new architecture that combines encoder-decoder architecture, multi-level integration, relation-aware global attention module. The encoder-decoder architecture is the main structure to extract deeper features. The multi-level integration constructs an asymmetric path that avoid information loss. The Relation-aware Global Attention module is used to enhance the network both channel-wise and spatial-wise. The architecture is trained and tested on SALICON 2017 benchmark and obtain competitive results compared with related research.
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Schauerte, B., Richarz, J., Fink, G.A.: Saliency-based identification and recognition of pointed-at objects. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, pp. 4638–4643 (2010). https://doi.org/10.1109/IROS.2010.5649430
Frintrop, S., Kessel, M.: Most salient region tracking. In: 2009 IEEE International Conference on Robotics and Automation, Kobe, pp. 1869–1874 (2009). https://doi.org/10.1109/ROBOT.2009.5152298
Saeko, T., Ramesh, R., Michael, G., Bruce, G.: Automatic image retargeting. In: Proceedings of the 4th International Conference on Mobile and Ubiquitous Multimedia, MUM 2005, vol. 154, pp. 59–68 (2005). https://doi.org/10.1145/1149488.1149499
Jiang, M., Huang, S., Duan, J., Zhao, Q.: SALICON: saliency in context. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 1072–1080 (2015). https://doi.org/10.1109/CVPR.2015.7298710
Reddy, N., Jain, S., Yarlagadda, P., Gandhi, V.: Tidying deep saliency prediction architectures (2020). abs/2003.04942
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. arXiv e-prints. arXiv:1505.04597 (2015)
Zhang, Z., Lan, C., Zeng, W., Jin, X., Chen, Z.: Relation-aware global attention for person re-identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 3183–3192 (2020). https://doi.org/10.1109/CVPR42600.2020.00325
Cao, G., Tang, Q., Jo, K.: Aggregated deep saliency prediction by self-attention network. In: Huang, D.-S., Premaratne, P. (eds.) ICIC 2020. LNCS (LNAI), vol. 12465, pp. 87–97. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60796-8_8
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998). https://doi.org/10.1109/34.730558
Bruce, N.D.B., Tsotsos, J.K.: Saliency, attention, and visual search: AN information theoretic approach. J. Vis. 9(3), 5 (2009)
Borji, A.: Boosting bottom-up and top-down visual features for saliency estimation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, pp. 438–445 (2012). https://doi.org/10.1109/CVPR.2012.6247706
Cornia, M., Baraldi, L., Serra, G., Cucchiara, R.: Predicting human eye fixations via an LSTM-based saliency attentive model. IEEE Trans. Image Process. 27(10), 5142–5154 (2018). https://doi.org/10.1109/TIP.2018.2851672
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv e-prints. arXiv:1409.1556 (2014)
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the government (MSIT) (No. 2020R1A2C2008972).
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Cao, G., Jo, KH. (2021). Saliency Prediction with Relation-Aware Global Attention Module. In: Jeong, H., Sumi, K. (eds) Frontiers of Computer Vision. IW-FCV 2021. Communications in Computer and Information Science, vol 1405. Springer, Cham. https://doi.org/10.1007/978-3-030-81638-4_25
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DOI: https://doi.org/10.1007/978-3-030-81638-4_25
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