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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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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
Guided search: An alternative to the feature integration model for visual search.
Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.15.3.419 · Cited by 2,147
Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206772 · Cited by 1,917
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
New benchmark for image segmentation evaluation
Ge, F., Wang, S., & Liu, T. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2762250 · Cited by 108
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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
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Wang, W., Shen, J., & Ling, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840724 · Cited by 348
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Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L. (2021) · Computational Visual Media · doi:10.1007/s41095-020-0199-z · Cited by 331
Self-citationComputer Vision and Image Processing: A Paper Review
Wiley, V., & Lucas, T. (2018) · International Journal of Artificial Intelligence Research · doi:10.29099/ijair.v2i1.42 · Cited by 298
Shakeri, R., Al-Garadi, M. A., Badawy, A., Mohamed, A., Khattab, T., Al-Ali, A. K., Harras, K. A., & Guizani, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924143 · Cited by 295
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Wang, W., Shen, J., Guo, F., Cheng, M.-M., & Borji, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00514 · Cited by 268
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Zhang, Q., Huang, N., Yao, L., Zhang, D., Shan, C., & Han, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2959253 · Cited by 247
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00990 · Cited by 231
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Hao, F., He, F., Cheng, J., Wang, L., Cao, J., & Tao, D. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00855 · Cited by 167
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Li, H., Su, X., Wang, J., Kan, H., Han, T., Zeng, Y., & Chai, X. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.11.001 · Cited by 59
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Zhang, R., Saran, A., Liu, B., Zhu, Y., Guo, S., Niekum, S., Ballard, D., & Hayhoe, M. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/689 · Cited by 48
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
Qiao, S., Shen, W., Qiu, W., Liu, C., & Yuille, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.199 · Cited by 41
Ham, S. W., Park, H.-C., Kim, E.-J., Kho, S.-Y., & Kim, D.-K. (2020) · Transportation Research Record: Journal of the Transportation Research Board · doi:10.1177/0361198120954187 · Cited by 9
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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