Abstract
Inspite of the huge success of Deep Neural Networks in object recognition, there are still situations in which they cannot reach human performance. In this work, the performance of an attention Deep Neural Network which is cued by important pixel of objects is addressed. First, the effect of color on accuracy of classification is evaluated. Then the network performance is compared with humans by using a set of images from different levels of revelation of important pixels. The results indicate that color information enhances the recognition of objects and there is a correspondence in accuracy of classification as well as correlation in decisions between human and attention networks at middle and low levels of important pixel revelation respectively.
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References
Bar, M., et al.: Top-down facilitation of visual recognition. Proc. Nat. Acad. Sci. 103(2), 449–454 (2006)
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)
Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 185–207 (2013)
Cadieu, C.F., et al.: Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS Comput. Biol. 10(12), e1003963 (2014)
Eberhardt, S., Cader, J.G., Serre, T.: How deep is the feature analysis underlying rapid visual categorization? In: Advances in Neural Information Processing Systems, pp. 1100–1108 (2016)
Egeth, H.E., Yantis, S.: Visual attention: control, representation, and time course. Annu. Rev. Psychol. 48(1), 269–297 (1997)
Geirhos, R., Janssen, D.H., Schütt, H.H., Rauber, J., Bethge, M., Wichmann, F.A.: Comparing deep neural networks against humans: object recognition when the signal gets weaker. arXiv preprint arXiv:1706.06969 (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision. Res. 40(10–12), 1489–1506 (2000)
Linsley, D., Scheibler, D., Eberhardt, S., Serre, T.: Global-and-local attention networks for visual recognition. arXiv preprint arXiv:1805.08819 (2018)
Linsley, D., Shiebler, D., Eberhardt, S., Karagounis, A., Serre, T.: Large-scale identification of the visual features used for object recognition with ClickMe.ai. J. Vision 18(10), 414–414 (2018)
Serre, T., Oliva, A., Poggio, T.: A feedforward architecture accounts for rapid categorization. Proc. Nat. Acad. Sci. 104(15), 6424–6429 (2007)
Torralba, A., Oliva, A., Castelhano, M.S., Henderson, J.M.: Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychol. Rev. 113(4), 766 (2006)
Ullman, S., Assif, L., Fetaya, E., Harari, D.: Atoms of recognition in human and computer vision. Proc. Nat. Acad. Sci. 113(10), 2744–2749 (2016)
Waldrop, M.M.: News feature: what are the limits of deep learning? Proc. Nat. Acad. Sci. 116(4), 1074–1077 (2019)
Acknowledgment
This research has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 665919. Thanks to Thomas Serre and Drew Linsley.
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Sadeghi, Z. (2020). An Investigation on Performance of Attention Deep Neural Networks in Rapid Object Recognition. In: Brito-Loeza, C., Espinosa-Romero, A., Martin-Gonzalez, A., Safi, A. (eds) Intelligent Computing Systems. ISICS 2020. Communications in Computer and Information Science, vol 1187. Springer, Cham. https://doi.org/10.1007/978-3-030-43364-2_1
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DOI: https://doi.org/10.1007/978-3-030-43364-2_1
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