The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Salient Object Detection: A Benchmark
Open accessAbstract
We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for the state-of-the-art models, provide useful hints toward constructing more challenging large-scale data sets and better saliency models. Finally, we propose probable solutions for tackling several open problems, such as evaluation scores and data set bias, which also suggest future research directions in the rapidly growing field of salient object detection.
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Cites
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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-citationAn on-chip photonic deep neural network for image classification
Ashtiani, F., Geers, A. J., & Aflatouni, F. (2022) · Nature · doi:10.1038/s41586-022-04714-0 · Cited by 699
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00861 · Cited by 423
Salient 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
Metadata
- Authors
- Borji, A., Cheng, M.-M., Jiang, H., & Li, J.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2015-12
- Volume
- 24
- Issue
- 12
- Pages
- 5706-5722
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W1772076007
- Cited by
- 1,364
- References
- 8
- FWCI
- 114.09
- Type
- journal article
- Language
- en
- Open access
- green
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