The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Shallow and Deep Convolutional Networks for Saliency Prediction
Open accessAbstract
The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train end-to-end architectures that are both fast and accurate. Two designs are proposed: a shallow convnet trained from scratch, and a another deeper solution whose first three layers are adapted from another network trained for classification. To the authors' knowledge, these are the first end-to-end CNNs trained and tested for the purpose of saliency prediction.
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Cites
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Cited by
Object Detection With Deep Learning: A Review
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DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations
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Predicting Human Eye Fixations via an LSTM-Based Saliency Attentive Model
Cornia, M., Baraldi, L., Serra, G., & Cucchiara, R. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2851672 · Cited by 474
Saliency in VR: How Do People Explore Virtual Environments?
Sitzmann, V., Serrano, A., Pavel, A., Agrawala, M., Gutierrez, D., Masia, B., & Wetzstein, G. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2793599 · Cited by 459
A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping
Wang, W., Shen, J., & Ling, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840724 · Cited by 348
Metadata
- Authors
- Pan, J., Sayrol, E., Giro-I-Nieto, X., McGuinness, K., & OConnor, N. E.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 598-606
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2288514685
- Cited by
- 471
- References
- 11
- FWCI
- 24.55
- Type
- conference paper
- Language
- en
- Open access
- green
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