Abstract
Predicting where humans look in a given scene is a well-known problem with multiple applications in consumer cameras, human–computer interaction, robotics, and gaming. With large-scale image datasets available for human fixation, it is now possible to train deep neural networks for generating a fixation map. Human fixations are a function of both local visual features and global context. We incorporate this in a deep neural network by using global and local features of an image to predict human fixations. We sample multi-scale features of the deep residual network and introduce a new method for incorporating these multi-scale features for the end-to-end training of our network. Our model DeepAttent obtains competitive results on SALICON and iSUN datasets and outperforms state-of-the-art methods on various metrics.
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References
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, pp. 1597–1604. IEEE (2009)
Borji, A., Tavakoli, H.R., Sihite, D.N., Itti, L.: Analysis of scores, datasets, and models in visual saliency prediction. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 921–928. IEEE (2013)
Buhrmester, M., Kwang, T., Gosling, S.D.: Amazon’s mechanical turk: a new source of inexpensive, yet high-quality, data? Perspect. Psychol. Sci. 6(1), 3–5 (2011)
Cerf, M., Frady, E.P., Koch, C.: Using semantic content as cues for better scanpath prediction. In: Proceedings of the 2008 symposium on Eye tracking research and applications, pp. 143–146. ACM (2008)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, pp. 248–255. IEEE (2009)
Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2008, pp. 1–8. IEEE (2008)
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)
Huang, X., Shen, C., Boix, X., Zhao, Q.: Salicon: reducing the semantic gap in saliency prediction by adapting deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 262–270 (2015)
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)
Jetley, S., Murray, N., Vig, E.: End-to-end saliency map** via probability distribution prediction. In: Proceedings of Computer Vision and Pattern Recognition 2016, pp. 5753–5761 (2016)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Jiang, M., Huang, S., Duan, J., Zhao, Q.: Salicon: saliency in context. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1072–1080. IEEE (2015)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2106–2113. IEEE (2009)
Kruthiventi, S.S., Ayush, K., Babu, R.V.: Deepfix: a fully convolutional neural network for predicting human eye fixations. IEEE Trans. Image Process. 26(9), 4446–4456 (2017)
Kümmerer, M., Theis, L., Bethge, M.: Deep gaze i: boosting saliency prediction with feature maps trained on imagenet. ar**v:1411.1045 (2014)
Kümmerer, M., Wallis, T.S., Bethge, M.: Information-theoretic model comparison unifies saliency metrics. Proc. Nat. Acad. Sci. 112(52), 16054–16059 (2015)
Le Meur, O., Le Callet, P., Barba, D.: Predicting visual fixations on video based on low-level visual features. Vision Res. 47(19), 2483–2498 (2007)
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp. 740–755. Springer (2014)
LSUN’16: Large-scale scene understanding challenge: Leaderboard (2016). http://lsun.cs.princeton.edu/leaderboard/index_2016.html#saliencysalicon. Last accessed 20 Apr 2018
Pan, J., Sayrol, E., Giro-i Nieto, X., McGuinness, K., O’Connor, N.E.: Shallow and deep convolutional networks for saliency prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 598–606 (2016)
Peters, R.J., Iyer, A., Itti, L., Koch, C.: Components of bottom-up gaze allocation in natural images. Vision Res. 45(18), 2397–2416 (2005)
Pinheiro, P.O., Lin, T.Y., Collobert, R., Dollár, P.: Learning to refine object segments. In: European Conference on Computer Vision, pp. 75–91. Springer (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ar**v:1409.1556 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., et al.: Going Deeper with Convolutions. In: CVPR (2015)
Vig, E., Dorr, M., Cox, D.: Large-scale optimization of hierarchical features for saliency prediction in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2798–2805 (2014)
**ao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3485–3492. IEEE (2010)
Xu, P., Ehinger, K.A., Zhang, Y., Finkelstein, A., Kulkarni, S.R., **ao, J.: Turkergaze: Crowdsourcing saliency with webcam based eye tracking. ar**v preprint ar**v:1504.06755 (2015)
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Dwivedi, K., Singh, N., Shanmugham, S.R., Kumar, M. (2020). DeepAttent: Saliency Prediction with Deep Multi-scale Residual Network. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_6
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DOI: https://doi.org/10.1007/978-981-32-9291-8_6
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