The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection
Abstract
How to incorporate cross-modal complementarity sufficiently is the cornerstone question for RGB-D salient object detection. Previous works mainly address this issue by simply concatenating multi-modal features or combining unimodal predictions. In this paper, we answer this question from two perspectives: (1) We argue that if the complementary part can be modelled more explicitly, the cross-modal complement is likely to be better captured. To this end, we design a novel complementarity-aware fusion (CA-Fuse) module when adopting the Convolutional Neural Network (CNN). By introducing cross-modal residual functions and complementarity-aware supervisions in each CA-Fuse module, the problem of learning complementary information from the paired modality is explicitly posed as asymptotically approximating the residual function. (2) Exploring the complement across all the levels. By cascading the CA-Fuse module and adding level-wise supervision from deep to shallow densely, the cross-level complement can be selected and combined progressively. The proposed RGB-D fusion network disambiguates both cross-modal and cross-level fusion processes and enables more sufficient fusion results. The experiments on public datasets show the effectiveness of the proposed CA-Fuse module and the RGB-D salient object detection network.
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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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,271
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Frequency-tuned salient region detection
Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206596 · Cited by 4,167
Harel, J., Koch, C., & Perona, P. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0073 · Cited by 3,439
Holistically-Nested Edge Detection
Xie, S., & Tu, Z. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.164 · Cited by 3,096
Global contrast based salient region detection
Cheng, M.-M., Zhang, G.-X., Mitra, N. J., Huang, X., & Hu, S.-M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995344 · Cited by 2,976
Global Contrast Based Salient Region Detection
Cheng, M.-M., Mitra, N. J., Huang, X., Torr, P. H. S., & Hu, S.-M. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345401 · Cited by 2,463
Cited by
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
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
Deep multimodal fusion for semantic image segmentation: A survey
Zhang, Y., Sidibé, D., Morel, O., & Mériaudeau, F. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104042 · Cited by 261
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
Metadata
- Authors
- Chen, H., & Li, Y.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 3051-3060
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2798857366
- Cited by
- 336
- References
- 51
- FWCI
- 13.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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