The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Learning Uncertain Convolutional Features for Accurate Saliency Detection
Open accessAbstract
Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which encourage the robustness and accuracy of saliency detection. We achieve this via introducing a reformulated dropout (R-dropout) after specific convolutional layers to construct an uncertain ensemble of internal feature units. In addition, we propose an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in our decoder network. The proposed methods can also be applied to other deep convolutional networks. Compared with existing saliency detection methods, the proposed UCF model is able to incorporate uncertainties for more accurate object boundary inference. Extensive experiments demonstrate that our proposed saliency model performs favorably against state-of-the-art approaches. The uncertain feature learning mechanism as well as the upsampling method can significantly improve performance on other pixel-wise vision tasks.
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Cites
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Cited by
EGNet: Edge Guidance Network for Salient Object Detection
Zhao, J., Liu, J.-J., Fan, D.-P., Cao, Y., Yang, J., & Cheng, M.-M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00887 · Cited by 1,202
Pyramid Feature Attention Network for Saliency Detection
Zhao, T., & Wu, X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00320 · Cited by 772
Dong, H., Song, K., He, Y., Xu, J., Yan, Y., & Meng, Q. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2958826 · Cited by 559
A Bi-Directional Message Passing Model for Salient Object Detection
Zhang, L., Dai, J., Lu, H., He, Y., & Wang, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00187 · Cited by 484
Self-citationSurface defect saliency of magnetic tile
Huang, Y., Qiu, C., & Yuan, K. (2020) · The Visual Computer · doi:10.1007/s00371-018-1588-5 · Cited by 438
Metadata
- Authors
- Zhang, P., Wang, D., Lu, H., Wang, H., & Yin, B.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 212-221
- OpenAlex
- W2963906836
- Cited by
- 481
- References
- 52
- FWCI
- 21.92
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, P., Wang, D., Lu, H., Wang, H., & Yin, B. (2017). Learning Uncertain Convolutional Features for Accurate Saliency Detection. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.32
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