The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
Open accessAbstract
Existing state-of-the-art salient object detection networks rely on aggregating multi-level features of pre-trained convolutional neural networks (CNNs). However, compared to high-level features, low-level features contribute less to performance. Meanwhile, they raise more computational cost because of their larger spatial resolutions. In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection. On the one hand, the framework constructs partial decoder which discards larger resolution features of shallow layers for acceleration. On the other hand, we observe that integrating features of deep layers will obtain relatively precise saliency map. Therefore we directly utilize generated saliency map to recurrently optimize features of deep layers. This strategy efficiently suppresses distractors in the features and significantly improves their representation ability. Experiments conducted on five benchmark datasets exhibit that the proposed model not only achieves state-of-the-art but also runs much faster than existing models. Besides, we apply the proposed framework to optimize existing multi-level feature aggregation models and significantly improve their efficiency and accuracy.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
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
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
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
EDN: Salient Object Detection via Extremely-Downsampled Network
Wu, Y.-H., Liu, Y., Zhang, L., Cheng, M.-M., & Ren, B. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3164550 · Cited by 282
RGB-T Salient Object Detection via Fusing Multi-Level CNN Features
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
MobileSal: Extremely Efficient RGB-D Salient Object Detection
Wu, Y.-H., Liu, Y., Xu, J., Bian, J.-W., Gu, Y.-C., & Cheng, M.-M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3134684 · Cited by 176
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Nguyen, D. C., Cheng, P., Ding, M., Lopez-Perez, D., Pathirana, P. N., Li, J., Seneviratne, A., Li, Y., & Poor, H. V. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3024783 · Cited by 160
Densely nested top-down flows for salient object detection
Fang, C., Tian, H., Zhang, D., Zhang, Q., Han, J., & Han, J. (2022) · Science China Information Sciences · doi:10.1007/s11432-021-3384-y · Cited by 98
Metadata
- Authors
- Wu, Z., Su, L., & Huang, Q.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3902-3911
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963112696
- Cited by
- 1,136
- References
- 5
- FWCI
- 44.44
- Type
- conference paper
- Language
- en
- Open access
- green
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