The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
Open accessAbstract
This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately-designed training process. In contrast, our JL-DCF learns from both RGB and depth inputs through a Siamese network. To this end, we propose two effective components: joint learning (JL), and densely-cooperative fusion (DCF). The JL module provides robust saliency feature learning, while the latter is introduced for complementary feature discovery. Comprehensive experiments on four popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization. As a result, JL-DCF significantly advances the top-1 D3Net model by an average of ~1.9% (S-measure) across six challenging datasets, showing that the proposed framework offers a potential solution for real-world applications and could provide more insight into the cross-modality complementarity task. The code will be available at https://github.com/kerenfu/JLDCF/.
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Cites
Deep Residual Learning for Image Recognition
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Cited by
Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., & Barnard, K. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00360 · Cited by 1,274
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
Self-citationHierarchical Alternate Interaction Network for RGB-D Salient Object Detection
Li, G., Liu, Z., Chen, M., Bai, Z., Lin, W., & Ling, H. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3062689 · Cited by 341
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
Self-citationMobileSal: 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
Metadata
- Authors
- Fu, K., Fan, D.-P., Ji, G.-P., & Zhao, Q.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 3049-3059
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035284915
- Cited by
- 365
- References
- 83
- FWCI
- 19.95
- Type
- conference paper
- Language
- en
- Open access
- green
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- Fu, K., Fan, D.-P., Ji, G.-P., & Zhao, Q. (2020). JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00312
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