The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Predicting eye fixations using convolutional neural networks
Abstract
It is believed that eye movements in free-viewing of natural scenes are directed by both bottom-up visual saliency and top-down visual factors. In this paper, we propose a novel computational framework to simultaneously learn these two types of visual features from raw image data using a multiresolution convolutional neural network (Mr-CNN) for predicting eye fixations. The Mr-CNN is directly trained from image regions centered on fixation and non-fixation locations over multiple resolutions, using raw image pixels as inputs and eye fixation attributes as labels. Diverse top-down visual features can be learned in higher layers. Meanwhile bottom-up visual saliency can also be inferred via combining information over multiple resolutions. Finally, optimal integration of bottom-up and top-down cues can be learned in the last logistic regression layer to predict eye fixations. The proposed approach achieves state-of-the-art results over four publically available benchmark datasets, demonstrating the superiority of our work.
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Cited by
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Self-citationObject Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
Liu, N., & Han, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.80 · Cited by 833
Self-citationNon-local Deep Features for Salient Object Detection
Luo, Z., Mishra, A., Achkar, A., Eichel, J., Li, S., & Jodoin, P.-M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.698 · Cited by 621
DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations
Kruthiventi, S. S. S., Ayush, K., & Babu, R. V. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2710620 · Cited by 478
Metadata
- Authors
- Liu, N., Han, J., Zhang, D., Wen, S., & Liu, T.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 362-370
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1948843088
- Cited by
- 201
- References
- 5
- FWCI
- 13.44
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, N., Han, J., Zhang, D., Wen, S., & Liu, T. (2015). Predicting eye fixations using convolutional neural networks. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298633
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