The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
Open accessAbstract
Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and fine details. Then it adaptively learns to combine these feature maps at each resolution and predict saliency maps with the combined features. Finally, the predicted results are efficiently fused to generate the final saliency map. In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework. By aggregating multi-level convolutional features in this efficient and flexible manner, the proposed saliency model provides accurate salient object labeling. Comprehensive experiments demonstrate that our method performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.
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Cited by
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
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
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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
RGB-D salient object detection: A survey
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
EDN: Salient Object Detection via Extremely-Downsampled Network
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Metadata
- Authors
- Zhang, P., Wang, D., Lu, H., Wang, H., & Ruan, X.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 202-211
- OpenAlex
- W2963032190
- Cited by
- 884
- References
- 10
- FWCI
- 29.12
- Type
- conference paper
- Language
- en
- Open access
- green
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