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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528
SUN: A Bayesian framework for saliency using natural statistics
Zhang, L., Tong, M. H., Marks, T. K., Shan, H., & Cottrell, G. W. (2008) · Journal of Vision · doi:10.1167/8.7.32 · Cited by 1,227
Deeply Supervised Salient Object Detection with Short Connections
Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., & Torr, P. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.563 · Cited by 1,143
Saliency detection by multi-context deep learning
Zhao, R., Ouyang, W., Li, H., & Wang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298731 · Cited by 995
Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Zhuowen Tu, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.5185 · Cited by 993
Deeplysupervised netsPreprint
Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2015) · Cited by 849
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
Liu, N., & Han, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.80 · Cited by 833
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
Song, S., & Xiao, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.94 · Cited by 681
RGBD Salient Object Detection: A Benchmark and Algorithms
Peng, H., Li, B., Xiong, W., Hu, W., & Ji, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_7 · Cited by 668
Multimodal deep learning for robust RGB-D object recognition
Eitel, A., Springenberg, J. T., Spinello, L., Riedmiller, M., & Burgard, W. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353446 · Cited by 631
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey
Han, J., Zhang, D., Cheng, G., Liu, N., & Xu, D. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2749125 · Cited by 621
Depth saliency based on anisotropic center-surround difference
Ju, R., Ge, L., Geng, W., Ren, T., & Wu, G. (2014) · 2014 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2014.7025222 · Cited by 523
Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework
Zhang, D., Meng, D., & Han, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2567393 · Cited by 508
Leveraging stereopsis for saliency analysis
Niu, Y., Geng, Y., Li, X., & Liu, F. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247708 · Cited by 479
Background Prior-Based Salient Object Detection via Deep Reconstruction Residual
Han, J., Zhang, D., Hu, X., Guo, L., Ren, J., & Wu, F. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2381471 · Cited by 444
CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion
Han, J., Chen, H., Liu, N., Yan, C., & Li, X. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2761775 · Cited by 423
Self-citationChen, Y., Li, J., Xiao, H., Jin, X., Yan, S., & Feng, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01629 · Cited by 397
RGBD Salient Object Detection via Deep Fusion
Qu, L., He, S., Zhang, J., Tian, J., Tang, Y., & Yang, Q. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2682981 · Cited by 360
Depth Enhanced Saliency Detection Method
Cheng, Y., Fu, H., Wei, X., Xiao, J., & Cao, X. (2014) · Proceedings of International Conference on Internet Multimedia Computing and Service · doi:10.1145/2632856.2632866 · Cited by 350
Region-Based Saliency Detection and Its Application in Object Recognition
Ren, Z., Gao, S., Chia, L.-T., & Tsang, I. W.-H. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2013.2280096 · Cited by 344
Top-down visual saliency via joint CRF and dictionary learning
Yang, J., & Yang, M.-H. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247940 · Cited by 320
RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation
Lee, S., Park, S.-J., & Hong, K.-S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.533 · Cited by 285
Real-Time Visual SLAM for Autonomous Underwater Hull Inspection Using Visual Saliency
Kim, A., & Eustice, R. M. (2013) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2235699 · Cited by 263
Gupta, S., Arbeláez, P., Girshick, R., & Malik, J. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0777-6 · Cited by 248
Locality-Sensitive Deconvolution Networks with Gated Fusion for RGB-D Indoor Semantic Segmentation
Cheng, Y., Cai, R., Li, Z., Zhao, X., & Huang, K. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.161 · Cited by 246
Song, H., Liu, Z., Du, H., Sun, G., Le Meur, O., & Ren, T. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2711277 · Cited by 244
Cong, R., Lei, J., Zhang, C., Huang, Q., Cao, X., & Hou, C. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2557347 · Cited by 235
Exploiting global priors for RGB-D saliency detection
Ren, J., Gong, X., Yu, L., Zhou, W., & Yang, M. Y. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301391 · Cited by 217
Local Background Enclosure for RGB-D Salient Object Detection
Feng, D., Barnes, N., You, S., & McCarthy, C. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.257 · Cited by 216
RGBD salient object detection via deep fusion
Qu, L., He, S., Zhang, J., Tian, J., Tang, Y., & Yang, Q. (2017) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 195
Saliency Detection for Stereoscopic Images
Fang, Y., Wang, J., Narwaria, M., Callet, P. L., & Lin, W. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2305100 · Cited by 171
Depth really Matters: Improving Visual Salient Region Detection with Depth
Desingh, K., K, M. K., Rajan, D., & Jawahar, C. (2013) · Procedings of the British Machine Vision Conference 2013 · doi:10.5244/c.27.98 · Cited by 171
Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining
Zhang, D., Han, J., Han, J., & Shao, L. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2495161 · Cited by 163
Cascaded Feature Network for Semantic Segmentation of RGB-D Images
Lin, D., Chen, G., Cohen‐Or, D., Heng, P., & Huang, H. F. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.147 · Cited by 155
Cong, R., Lei, J., Fu, H., Huang, Q., Cao, X., & Hou, C. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2763819 · Cited by 137
Ciptadi, A., Hermans, T., & Rehg, J. M. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.112 · Cited by 124
Discriminative Multi-modal Feature Fusion for RGBD Indoor Scene Recognition
Zhu, H., Weibel, J.-B., & Lu, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.324 · Cited by 117
Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks
Liu, N., Han, J., Liu, T., & Li, X. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2628878 · Cited by 112
MMSS: Multi-modal Sharable and Specific Feature Learning for RGB-D Object Recognition
Wang, A., Cai, J., Lu, J., & Cham, T. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.134 · Cited by 98
Salient region detection for stereoscopic images
Fan, X., Liu, Z., & Sun, G. (2014) · doi:10.1109/icdsp.2014.6900706 · Cited by 82
Specific object retrieval based on salient regions
Shao, L., & Brady, M. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.04.010 · Cited by 46
Cited by
An Underwater Image Enhancement Benchmark Dataset and Beyond
Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., & Tao, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2955241 · Cited by 2,373
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
A Single Stream Network for Robust and Real-Time RGB-D Salient Object Detection
Zhao, X., Zhang, L., Pang, Y., Lu, H., & Zhang, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58542-6_39 · Cited by 259
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
