The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Deeply Supervised Salient Object Detection with Short Connections
Abstract
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and saliency detection algorithms developed lately have been mostly based on Fully Convolutional Neural Networks (FCNs). There is still a large room for improvement over the generic FCN models that do not explicitly deal with the scale-space problem. Holisitcally-Nested Edge Detector (HED) provides a skip-layer structure with deep supervision for edge and boundary detection, but the performance gain of HED on saliency detection is not obvious. In this paper, we propose a new saliency method by introducing short connections to the skip-layer structures within the HED architecture. Our framework provides rich multi-scale feature maps at each layer, a property that is critically needed to perform segment detection. Our method produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency (0.08 seconds per image), effectiveness, and simplicity over the existing algorithms.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511
Cited by
Object 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
Structure-Measure: A New Way to Evaluate Foreground Maps
Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., & Borji, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.487 · Cited by 2,092
Self-citationSalient Object Detection via Integrity Learning
Zhuge, M., Fan, D.-P., Liu, N., Zhang, D., Xu, D., & Shao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3179526 · Cited by 403
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection
Chen, H., & Li, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00322 · Cited by 336
Roth, H. R., Lu, L., Lay, N., Harrison, A. P., Farag, A., Sohn, A., & Summers, R. M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.006 · Cited by 325
Metadata
- Authors
- Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., & Torr, P.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 5300-5309
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963299740
- Cited by
- 1,143
- References
- 4
- FWCI
- 39.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., & Torr, P. (2017). Deeply Supervised Salient Object Detection with Short Connections. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.563
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