11.2.4.4 RGB-D Salient Object Segmentation and Detection

Chapter Contents (Back)
Range Segmentation. RGB-D Segmentation. Salient Objects.
See also Range and Color, RGB-D Segmentation and Analysis.
See also Salient Regions, Saliencey for Regions.
See also Depth Object Segmentation, Point Cloud Segmentation.
See also Semantic Object Detection, 3D, Depth.

Cong, R.M.[Run-Min], Lei, J.J.[Jian-Jun], Zhang, C.Q.[Chang-Qing], Huang, Q.M.[Qing-Ming], Cao, X.C.[Xiao-Chun], Hou, C.P.[Chun-Ping],
Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion,
SPLetters(23), No. 6, June 2016, pp. 819-823.
IEEE DOI 1606
Computational modeling BibRef

Cong, R.M.[Run-Min], Lei, J.J.[Jian-Jun], Fu, H.Z.[Hua-Zhu], Huang, Q.M.[Qing-Ming], Cao, X.C.[Xiao-Chun], Hou, C.P.[Chun-Ping],
Co-Saliency Detection for RGBD Images Based on Multi-Constraint Feature Matching and Cross Label Propagation,
IP(27), No. 2, February 2018, pp. 568-579.
IEEE DOI 1712
Computational modeling, Feature extraction, Image segmentation, Optimization, Robustness, multi-constraint BibRef

Cong, R.M.[Run-Min], Lei, J.J.[Jian-Jun], Fu, H.Z.[Hua-Zhu], Porikli, F.M.[Fatih M.], Huang, Q.M.[Qing-Ming], Hou, C.P.[Chun-Ping],
Video Saliency Detection via Sparsity-Based Reconstruction and Propagation,
IP(28), No. 10, October 2019, pp. 4819-4831.
IEEE DOI 1909
feature extraction, image motion analysis, image reconstruction, image sampling, image sequences, object detection, global optimization BibRef

Zhang, T.H.[Tian-Hao], Zhou, Y.[Yuan], Huo, S.W.[Shu-Wei], Hou, C.P.[Chun-Ping],
Label propagation based saliency detection via graph design,
ICIP17(460-464)
IEEE DOI 1803
Color, Image color analysis, Image edge detection, Reliability, Saliency detection, Task analysis, Visualization, Saliency detection BibRef

Cong, R.M.[Run-Min], Lei, J.J.[Jian-Jun], Fu, H.Z.[Hua-Zhu], Huang, Q.M.[Qing-Ming], Cao, X.C.[Xiao-Chun], Ling, N.[Nam],
HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images,
MultMed(21), No. 7, July 2019, pp. 1660-1671.
IEEE DOI 1906
Saliency detection, Image reconstruction, Dictionaries, Feature extraction, Task analysis, energy function refinement BibRef

Cong, R.M.[Run-Min], Lei, J.J.[Jian-Jun], Fu, H.Z.[Hua-Zhu], Hou, J.H.[Jun-Hui], Huang, Q.M.[Qing-Ming], Kwong, S.[Sam],
Going From RGB to RGBD Saliency: A Depth-Guided Transformation Model,
Cyber(50), No. 8, August 2020, pp. 3627-3639.
IEEE DOI 2007
Saliency detection, Image color analysis, Optimization, Shape, Feature extraction, Task analysis, Object detection, Depth cue, transformation model BibRef

Li, C.Y.[Chong-Yi], Cong, R.M.[Run-Min], Piao, Y.[Yongri], Xu, Q.Q.[Qian-Qian], Loy, C.C.[Chen Change],
RGB-D Salient Object Detection with Cross-modality Modulation and Selection,
ECCV20(VIII:225-241).
Springer DOI 2011
BibRef

Zhang, Q.[Qiang], Xiao, T.L.[Tong-Lin], Huang, N.C.[Nian-Chang], Zhang, D.W.[Ding-Wen], Han, J.G.[Jun-Gong],
Revisiting Feature Fusion for RGB-T Salient Object Detection,
CirSysVideo(31), No. 5, 2021, pp. 1804-1818.
IEEE DOI 2105
BibRef

Huang, N.C.[Nian-Chang], Liu, Y.[Yi], Zhang, Q.[Qiang], Han, J.G.[Jun-Gong],
Joint Cross-Modal and Unimodal Features for RGB-D Salient Object Detection,
MultMed(23), 2021, pp. 2428-2441.
IEEE DOI 2108
Feature extraction, Saliency detection, Object detection, Computational modeling, Task analysis, multi-branch feature fusion and feature selection BibRef

Huang, N.Z.[Nian-Zchang], Luo, Y.J.[Yong-Jiang], Zhang, Q.[Qiang], Han, J.G.[Jun-Gong],
Discriminative unimodal feature selection and fusion for RGB-D salient object detection,
PR(122), 2022, pp. 108359.
Elsevier DOI 2112
RGB-D salient object detection, Discriminative unimodal feature selection, Multi-scale cross-modal feature fusion BibRef

Li, C.Y.[Chong-Yi], Cong, R.M.[Run-Min], Kwong, S.[Sam], Hou, J.H.[Jun-Hui], Fu, H.Z.[Hua-Zhu], Zhu, G.P.[Guo-Pu], Zhang, D.W.[Ding-Wen], Huang, Q.M.[Qing-Ming],
ASIF-Net: Attention Steered Interweave Fusion Network for RGB-D Salient Object Detection,
Cyber(51), No. 1, January 2021, pp. 88-100.
IEEE DOI 2012
Feature extraction, Saliency detection, Object detection, Task analysis, Fuses, Random access memory, Semantics, saliency detection BibRef

Wen, H.F.[Hong-Fa], Yan, C.G.[Cheng-Gang], Zhou, X.F.[Xiao-Fei], Cong, R.M.[Run-Min], Sun, Y.Q.[Yao-Qi], Zheng, B.[Bolun], Zhang, J.Y.[Ji-Yong], Bao, Y.J.[Yong-Jun], Ding, G.G.[Gui-Guang],
Dynamic Selective Network for RGB-D Salient Object Detection,
IP(30), 2021, pp. 9179-9192.
IEEE DOI 2112
Feature extraction, Semantics, Object detection, Deep learning, Saliency detection, Logic gates, Computer architecture, feature fusion BibRef

Song, H.[Hangke], Liu, Z.[Zhi], Xie, Y.F.[Yu-Feng], Wu, L.S.[Li-Shan], Huang, M.[Mengke],
RGBD Co-Saliency Detection via Bagging-Based Clustering,
SPLetters(23), No. 12, December 2016, pp. 1722-1726.
IEEE DOI 1612
feature extraction BibRef

Tang, Y.L.[Yan-Long], Tong, R.[Ruofeng], Tang, M.[Min], Zhang, Y.[Yun],
Depth incorporating with color improves salient object detection,
VC(32), No. 1, January 2016, pp. 111-121.
WWW Link. 1602
BibRef

Ye, L.W.[Lin-Wei], Liu, Z.[Zhi], Li, L.[Lina], Shen, L.Q.[Li-Quan], Bai, C.[Cong], Wang, Y.[Yang],
Salient Object Segmentation via Effective Integration of Saliency and Objectness,
MultMed(19), No. 8, August 2017, pp. 1742-1756.
IEEE DOI 1708
Benchmark testing, Computer science, Image color analysis, Image edge detection, Image segmentation, Object segmentation, Predictive models, Graph-based integration, object probability, objectness map, saliency map, salient, object, segmentation BibRef

Song, H.K.[Hang-Ke], Liu, Z.[Zhi], Du, H.[Huan], Sun, G.L.[Guang-Ling], Le Meur, O.[Olivier], Ren, T.W.[Tong-Wei],
Depth-Aware Salient Object Detection and Segmentation via Multiscale Discriminative Saliency Fusion and Bootstrap Learning,
IP(26), No. 9, September 2017, pp. 4204-4216.
IEEE DOI 1708
computer bootstrapping, feature extraction, image colour analysis, image fusion, image segmentation, learning (artificial intelligence), regression analysis, stereo image processing, DSF saliency map, depth maps, depth-aware salient object detection and segmentation framework, discriminative saliency fusion, high-level location priors, mid-level feature weighted factors, multiscale region segmentation, random forest regressor, discriminative saliency fusion BibRef

Imamoglu, N.[Nevrez], Shimoda, W.[Wataru], Zhang, C.[Chi], Fang, Y.M.[Yu-Ming], Kanezaki, A.[Asako], Yanai, K.[Keiji], Nishida, Y.[Yoshifumi],
An integration of bottom-up and top-down salient cues on RGB-D data: saliency from objectness versus non-objectness,
SIViP(12), No. 2, February 2018, pp. 307-314.
Springer DOI 1802
BibRef

Xu, H.B.[Hai-Bo], Zhang, G.[Ge], Zhang, Q.M.[Qing-Ming],
Retracted: An iterative propagation based co-saliency framework for RGBD images,
JVCIR(77), 2021, pp. 103083.
Elsevier DOI 2106
BibRef
And: Originally: JVCIR(59), 2019, pp. 186-194.
Elsevier DOI 1903
RGBD images, Co-saliency, Iterative optimization, Saliency propagation, Depth information, Saliency detection BibRef

Jiang, Y.[Yibo], Bi, H.[Hui], Li, H.[Hui], Xu, Z.H.[Zhi-Hao],
Automatic and Accurate 3D Measurement Based on RGBD Saliency Detection,
IEICE(E102-D), No. 3, March 2019, pp. 688-689.
WWW Link. 1904
BibRef

Ding, Y.[Yu], Liu, Z.[Zhi], Huang, M.[Mengke], Shi, R.[Ran], Wang, X.Y.[Xiang-Yang],
Depth-aware saliency detection using convolutional neural networks,
JVCIR(61), 2019, pp. 1-9.
Elsevier DOI 1906
Saliency detection, Convolutional neural networks, Depth saliency network, Saliency fusion network, RGBD images, Stereoscopic images BibRef

Yuan, J.[Jing], Cao, Y.[Yang], Kang, Y.[Yu], Song, W.G.[Wei-Guo], Yin, Z.C.[Zhong-Cheng], Ba, R.[Rui], Ma, Q.[Qing],
3D Layout encoding network for spatial-aware 3D saliency modelling,
IET-CV(13), No. 5, August 2019, pp. 480-488.
DOI Link 1908
RGB-D saliency, deal with low quality D from such sensors. BibRef

Chen, C., Wei, J., Peng, C., Zhang, W., Qin, H.,
Improved Saliency Detection in RGB-D Images Using Two-Phase Depth Estimation and Selective Deep Fusion,
IP(29), 2020, pp. 4296-4307.
IEEE DOI 2002
RGB-D saliency detection, inter-image correspondences, low-level saliency, selective deep fusion BibRef

Pan, L.[Liang], Zhou, X.F.[Xiao-Fei], Shi, R.[Ran], Zhang, J.Y.[Ji-Yong], Yan, C.G.[Cheng-Gang],
Cross-modal feature extraction and integration based RGBD saliency detection,
IVC(101), 2020, pp. 103964.
Elsevier DOI 2009
RGBD, Saliency, Cross-modal, Feature extraction, Integration BibRef

Luo, A.[Ao], Li, X.[Xin], Yang, F.[Fan], Jiao, Z.C.[Zhi-Cheng], Cheng, H.[Hong], Lyu, S.W.[Si-Wei],
Cascade Graph Neural Networks for RGB-D Salient Object Detection,
ECCV20(XII: 346-364).
Springer DOI 2010
BibRef

Niu, Y., Long, G., Liu, W., Guo, W., He, S.,
Boundary-Aware RGBD Salient Object Detection With Cross-Modal Feature Sampling,
IP(29), 2020, pp. 9496-9507.
IEEE DOI 1806
Object detection, Image color analysis, Merging, Feature extraction, Fuses, Image edge detection, Estimation, boundary-aware estimation BibRef

Liang, F.F.[Fang-Fang], Duan, L.[Lijuan], Ma, W.[Wei], Qiao, Y.[Yuanhua], Miao, J.[Jun], Ye, Q.X.[Qi-Xiang],
Context-aware network for RGB-D salient object detection,
PR(111), 2021, pp. 107630.
Elsevier DOI 2012
Stereoscopic saliency analysis, 3D images, Multi-modal context fusion, Context-dependent deconvolution BibRef

Liang, F.F.[Fang-Fang], Duan, L.[Lijuan], Ma, W.[Wei], Qiao, Y.[Yuanhua], Cai, Z.[Zhi], Miao, J.[Jun], Ye, Q.X.[Qi-Xiang],
CoCNN: RGB-D deep fusion for stereoscopic salient object detection,
PR(104), 2020, pp. 107329.
Elsevier DOI 2005
Coupled CNN, Cascaded span network, Stereoscopic images, Salient object detection BibRef

Huang, R., Xing, Y., Zou, Y.,
Triple-Complementary Network for RGB-D Salient Object Detection,
SPLetters(27), 2020, pp. 775-779.
IEEE DOI 2006
RGB-D saliency detection, saliency fusion, triple-complementary network BibRef

Zhou, W.J.[Wu-Jie], Chen, Y.Z.[Yu-Zhen], Liu, C.[Chang], Yu, L.[Lu],
GFNet: Gate Fusion Network With Res2Net for Detecting Salient Objects in RGB-D Images,
SPLetters(27), 2020, pp. 800-804.
IEEE DOI 2006
Logic gates, Feature extraction, Convolution, Training, Detectors, Object detection, Decoding, Gate fusion network, gate mechanism, salient object detection BibRef

Zhou, Y.[Yang], Liu, X.Q.[Xiao-Qi], Zhang, Y.[Yun], Yin, H.B.[Hai-Bing], Lu, Y.[Yu],
Salient object detection via reliability-based depth compactness and depth contrast,
IET-IPR(14), No. 14, December 2020, pp. 3623-3631.
DOI Link 2012
BibRef

Guo, Q.L.[Qin-Ling], Zhou, W.[Wujie], Lei, J.S.[Jing-Sheng], Yu, L.[Lu],
TSFNet: Two-Stage Fusion Network for RGB-T Salient Object Detection,
SPLetters(28), 2021, pp. 1655-1659.
IEEE DOI 2109
Feature extraction, Object detection, Computational modeling, Image segmentation, Encoding, Decoding, Benchmark testing, RGB-T, feature-wise fusion module BibRef

Zhou, W.[Wujie], Guo, Q.L.[Qin-Ling], Lei, J.S.[Jing-Sheng], Yu, L.[Lu], Hwang, J.N.[Jenq-Neng],
ECFFNet: Effective and Consistent Feature Fusion Network for RGB-T Salient Object Detection,
CirSysVideo(32), No. 3, March 2022, pp. 1224-1235.
IEEE DOI 2203
Feature extraction, Decoding, Streaming media, Imaging, Sorting, Meteorology, Lighting, RGB-T data, salient object detection, multilevel consistent fusion module BibRef

Zhou, W.[Wujie], Zhu, Y.[Yun], Lei, J.S.[Jing-Sheng], Wan, J.[Jian], Yu, L.[Lu],
CCAFNet: Crossflow and Cross-Scale Adaptive Fusion Network for Detecting Salient Objects in RGB-D Images,
MultMed(24), No. 2022, pp. 2192-2204.
IEEE DOI 2204
Feature extraction, Semantics, Adaptation models, Data mining, Streaming media, Predictive models, Logic gates, spatial fusion module BibRef

Zhou, W.[Wujie], Pan, S.[Sijia], Lei, J.S.[Jing-Sheng], Yu, L.[Lu],
MRINet: Multilevel Reverse-Context Interactive-Fusion Network for Detecting Salient Objects in RGB-D Images,
SPLetters(28), 2021, pp. 1525-1529.
IEEE DOI 2108
Feature extraction, Fuses, Convolution, Semantics, Saliency detection, Magnetic resonance imaging, Training, deep learning BibRef

Zhang, X.Y.[Xin-Yue], Jin, T.[Ting], Zhou, W.J.[Wu-Jie], Lei, J.S.[Jing-Sheng],
Attention-based contextual interaction asymmetric network for RGB-D saliency prediction,
JVCIR(74), 2021, pp. 102997.
Elsevier DOI 2101
RGB-D image, Saliency prediction, Attention mechanism, Contextual interaction BibRef

Chen, Q.[Qian], Fu, K.[Keren], Liu, Z.[Ze], Chen, G.[Geng], Du, H.W.[Hong-Wei], Qiu, B.[Bensheng], Shao, L.[Ling],
EF-Net: A novel enhancement and fusion network for RGB-D saliency detection,
PR(112), 2021, pp. 107740.
Elsevier DOI 2102
Salient object detection, RGB-D image, Depth enhancement, Feature fusion BibRef

Chen, C., Wei, J., Peng, C., Qin, H.,
Depth-Quality-Aware Salient Object Detection,
IP(30), 2021, pp. 2350-2363.
IEEE DOI 2102
image colour analysis, image fusion, object detection, fusion-based RGB-D salient object detection, weakly supervised learning BibRef

Liu, D.[Di], Zhang, K.[Kao], Chen, Z.Z.[Zhen-Zhong],
Attentive Cross-Modal Fusion Network for RGB-D Saliency Detection,
MultMed(23), 2021, pp. 967-981.
IEEE DOI 2103
Object detection, Saliency detection, Feature extraction, Fuses, Visualization, Computational modeling, Semantics, RGB-D salient object detection BibRef

Liu, D.[Di], Hu, Y.[Yaosi], Zhang, K.[Kao], Chen, Z.Z.[Zhen-Zhong],
Two-Stream Refinement Network for RGB-D Saliency Detection,
ICIP19(3925-3929)
IEEE DOI 1910
Two-stream Network, Deep Learning, Fusion Refinement Module, RGB-D Saliency, Propagation-based Refinement BibRef

Zhou, W.[Wujie], Lv, Y.[Ying], Lei, J.S.[Jing-Sheng], Yu, L.[Lu],
Global and Local-Contrast Guides Content-Aware Fusion for RGB-D Saliency Prediction,
SMCS(51), No. 6, June 2021, pp. 3641-3649.
IEEE DOI 2106
Feature extraction, Predictive models, Fuses, Convolution, Visualization, Image resolution, Deep learning, Contrast feature, RGB-D saliency prediction BibRef

Zhu, Y.[Yun], Zhou, W.[Wujie], Li, Q.[Qiang], Yu, L.[Lu],
Parallax-Estimation-Enhanced Network With Interweave Consistency Feature Fusion for Binocular Salient Object Detection,
SPLetters(28), 2021, pp. 927-931.
IEEE DOI 2106
Hafnium, Parallax estimation enhancement, salient object detection, interweave consistency fusion, binocular image BibRef

Chen, H.[Hao], Li, Y.F.[You-Fu], Deng, Y.J.[Yong-Jian], Lin, G.[Guosheng],
CNN-Based RGB-D Salient Object Detection: Learn, Select, and Fuse,
IJCV(129), No. 7, July 2021, pp. 2076-2096.
Springer DOI 2106
BibRef

Deng, S.[Shuang], Dong, Q.[Qiulei],
GA-NET: Global Attention Network for Point Cloud Semantic Segmentation,
SPLetters(28), 2021, pp. 1300-1304.
IEEE DOI 2107
Feature extraction, Semantics, Computational complexity, Vegetation mapping, Image segmentation, deep learning BibRef

Chen, Z.[Zuyao], Cong, R.[Runmin], Xu, Q.Q.[Qian-Qian], Huang, Q.M.[Qing-Ming],
DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection,
IP(30), 2021, pp. 7012-7024.
IEEE DOI 2108
Logic gates, Object detection, Contamination, Task analysis, Saliency detection, Computer science, Image color analysis, gated multi-modality attention BibRef

Zhou, W.J.[Wu-Jie], Wu, J.W.[Jun-Wei], Lei, J.S.[Jing-Sheng], Hwang, J.N.[Jenq-Neng], Yu, L.[Lu],
Salient Object Detection in Stereoscopic 3D Images Using a Deep Convolutional Residual Autoencoder,
MultMed(23), 2021, pp. 3388-3399.
IEEE DOI 2109
Object detection, Feature extraction, Saliency detection, Computer architecture, convolutional residual neural networks BibRef

Yao, C.[Cuili], Feng, L.[Lin], Kong, Y.[Yuqiu], Li, S.M.[Sheng-Ming], Li, H.[Hang],
Double cross-modality progressively guided network for RGB-D salient object detection,
IVC(117), 2022, pp. 104351.
Elsevier DOI 2112
RGB-D, Salient object detection, Cross-modality, Attention mechanism, Integration BibRef

Wang, X.Q.[Xiao-Qiang], Zhu, L.[Lei], Tang, S.L.[Si-Liang], Fu, H.Z.[Hua-Zhu], Li, P.[Ping], Wu, F.[Fei], Yang, Y.[Yi], Zhuang, Y.T.[Yue-Ting],
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images,
IP(31), 2022, pp. 1107-1119.
IEEE DOI 2201
Feature extraction, Saliency detection, Estimation, Fuses, Object detection, Convolutional neural networks, Training, attention consistency BibRef

Wang, F.[Fengyun], Pan, J.[Jinshan], Xu, S.[Shoukun], Tang, J.H.[Jin-Hui],
Learning Discriminative Cross-Modality Features for RGB-D Saliency Detection,
IP(31), 2022, pp. 1285-1297.
IEEE DOI 2202
Feature extraction, Correlation, Saliency detection, Fuses, Convolution, Task analysis, Object detection, correlation-fusion BibRef

Zhu, J.[Jinchao], Zhang, X.Y.[Xiao-Yu], Fang, X.[Xian], Dong, F.[Feng], Qiu, Y.[Yu],
Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection,
SPLetters(29), 2022, pp. 359-363.
IEEE DOI 2202
Mixers, Logic gates, Decoding, Feature extraction, Semantics, Training, Object detection, Salient object detection, multi-modal, feature fusion BibRef

Zhou, X.F.[Xiao-Fei], Wen, H.F.[Hong-Fa], Shi, R.[Ran], Yin, H.B.[Hai-Bing], Zhang, J.Y.[Ji-Yong], Yan, C.G.[Cheng-Gang],
FANet: Feature aggregation network for RGBD saliency detection,
SP:IC(102), 2022, pp. 116591.
Elsevier DOI 2202
RGBD saliency, Feature aggregation, Graph neural networks, Hierarchical fusion BibRef

Zhang, Y.F.[Yuan-Fang], Zheng, J.B.[Jiang-Bin], Jia, W.J.[Wen-Jing], Huang, W.F.[Wen-Feng], Li, L.[Long], Liu, N.[Nianz], Li, F.[Fei], He, X.J.[Xiang-Jian],
Deep RGB-D Saliency Detection Without Depth,
MultMed(24), 2022, pp. 755-767.
IEEE DOI 2202
Feature extraction, Saliency detection, Fuses, Decoding, Computational modeling, Predictive models, Visualization, saliency detection BibRef

Huang, N.Z.[Nian-Zchang], Yang, Y.[Yang], Zhang, D.W.[Ding-Wen], Zhang, Q.[Qiang], Han, J.G.[Jun-Gong],
Employing Bilinear Fusion and Saliency Prior Information for RGB-D Salient Object Detection,
MultMed(24), 2022, pp. 1651-1664.
IEEE DOI 2204
Feature extraction, Saliency detection, Cognition, Task analysis, Object detection, Computational modeling, Visualization, saliency refinement and prediction BibRef

Gao, W.[Wei], Liao, G.[Guibiao], Ma, S.W.[Si-Wei], Li, G.[Ge], Liang, Y.S.[Yong-Sheng], Lin, W.S.[Wei-Si],
Unified Information Fusion Network for Multi-Modal RGB-D and RGB-T Salient Object Detection,
CirSysVideo(32), No. 4, April 2022, pp. 2091-2106.
IEEE DOI 2204
Feature extraction, Task analysis, Visualization, Object detection, Image color analysis, Decoding, Bidirectional control, salient object detection BibRef

Zhang, Q.[Qiang], Duanmu, M.X.[Ming-Xing], Luo, Y.J.[Yong-Jiang], Liu, Y.[Yi], Han, J.G.[Jun-Gong],
Engaging Part-Whole Hierarchies and Contrast Cues for Salient Object Detection,
CirSysVideo(32), No. 6, June 2022, pp. 3644-3658.
IEEE DOI 2206
Object detection, Feature extraction, Routing, Noise measurement, Semantics, Saliency detection, Image segmentation, attention BibRef

Feng, G.[Guang], Meng, J.[Jinyu], Zhang, L.H.[Li-He], Lu, H.C.[Hu-Chuan],
Encoder deep interleaved network with multi-scale aggregation for RGB-D salient object detection,
PR(128), 2022, pp. 108666.
Elsevier DOI 2205
RGB-D salient object detection, Deep interleaved encoder, Cross-modal mutual guidance, Real-time
See also Learning to Detect Salient Objects with Image-Level Supervision. BibRef

Pang, Y.W.[You-Wei], Zhao, X.Q.[Xiao-Qi], Zhang, L.H.[Li-He], Lu, H.C.[Hu-Chuan],
Multi-Scale Interactive Network for Salient Object Detection,
CVPR20(9410-9419)
IEEE DOI 2008
Feature extraction, Object detection, Aggregates, Spatial coherence, Training, Task analysis, Decoding BibRef

Wang, P.J.[Peng-Jie], Liu, Y.X.[Yu-Xuan], Cao, Y.[Ying], Yang, X.[Xin], Luo, Y.[Yu], Lu, H.C.[Hu-Chuan], Liang, Z.J.[Zi-Jian], Lau, R.W.H.[Rynson W.H.],
Salient object detection with image-level binary supervision,
PR(129), 2022, pp. 108782.
Elsevier DOI 2206
Weak supervision, Salient object detection, Binary labels
See also Encoder deep interleaved network with multi-scale aggregation for RGB-D salient object detection. BibRef

Tian, X.[Xin], Xu, K.[Ke], Yang, X.[Xin], Yin, B.C.[Bao-Cai], Lau, R.W.H.[Rynson W. H.],
Learning to Detect Instance-Level Salient Objects Using Complementary Image Labels,
IJCV(130), No. 3, March 2022, pp. 729-746.
Springer DOI 2203
BibRef

Wang, L.J.[Li-Jun], Lu, H.C.[Hu-Chuan], Wang, Y.F.[Yi-Fan], Feng, M.Y.[Meng-Yang], Wang, D.[Dong], Yin, B.C.[Bao-Cai], Ruan, X.[Xiang],
Learning to Detect Salient Objects with Image-Level Supervision,
CVPR17(3796-3805)
IEEE DOI 1711
Computational modeling, Detectors, Object detection, Semantics, Supervised learning, Training
See also Encoder deep interleaved network with multi-scale aggregation for RGB-D salient object detection. BibRef

Zhang, M.[Miao], Liu, J.[Jie], Wang, Y.F.[Yi-Fei], Piao, Y.R.[Yong-Ri], Yao, S.Y.[Shun-Yu], Ji, W.[Wei], Li, J.J.[Jing-Jing], Lu, H.C.[Hu-Chuan], Luo, Z.X.[Zhong-Xuan],
Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection,
ICCV21(1533-1543)
IEEE DOI 2203
Convolution, Computational modeling, Object detection, Streaming media, Information filters, Real-time systems, Low-level and physics-based vision BibRef

Pang, Y.W.[You-Wei], Zhang, L.H.[Li-He], Zhao, X.Q.[Xiao-Qi], Lu, H.C.[Hu-Chuan],
Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection,
ECCV20(XXV:235-252).
Springer DOI 2011
BibRef

Zhang, H.S.[Hong-Shuang], Zeng, Y.[Yu], Lu, H.C.[Hu-Chuan], Zhang, L.[Lihe], Li, J.H.[Jian-Hua], Qi, J.Q.[Jin-Qing],
Learning to Detect Salient Object With Multi-Source Weak Supervision,
PAMI(44), No. 7, July 2022, pp. 3577-3589.
IEEE DOI 2206
Saliency detection, Annotations, Image segmentation, Dogs, Feature extraction, Task analysis, Noise measurement, Saliency, weak supervision BibRef

Qian, M.Y.[Ming-Yang], Qi, J.Q.[Jin-Qing], Zhang, L.[Lihe], Feng, M.Y.[Meng-Yang], Lu, H.C.[Hu-Chuan],
Language-aware weak supervision for salient object detection,
PR(96), 2019, pp. 106955.
Elsevier DOI 1909
Saliency detection, Natural language, Textual-visual pairwise, Self-supervision BibRef

Zhang, L.H.[Li-He], Sun, J.Y.[Jia-Yu], Wang, T.T.[Tian-Tian], Min, Y.F.[Yi-Fan], Lu, H.C.[Hu-Chuan],
Visual Saliency Detection via Kernelized Subspace Ranking With Active Learning,
IP(29), 2020, pp. 2258-2270.
IEEE DOI 2001
Saliency detection, Proposals, Visualization, Task analysis, Training, Uncertainty, Feature extraction, Saliency detection, feature projection BibRef

Wang, T.T.[Tian-Tian], Zhang, L.H.[Li-He], Lu, H.C.[Hu-Chuan], Sun, C.[Chong], Qi, J.Q.[Jin-Qing],
Kernelized Subspace Ranking for Saliency Detection,
ECCV16(VIII: 450-466).
Springer DOI 1611
BibRef

Zhang, P.P.[Ping-Ping], Wang, D.[Dong], Lu, H.C.[Hu-Chuan], Wang, H.Y.[Hong-Yu], Yin, B.,
Learning Uncertain Convolutional Features for Accurate Saliency Detection,
ICCV17(212-221)
IEEE DOI 1802
image segmentation, learning (artificial intelligence), neural nets, Uncertainty BibRef

Zhang, P.P.[Ping-Ping], Wang, D.[Dong], Lu, H.C.[Hu-Chuan], Wang, H.Y.[Hong-Yu], Ruan, X.[Xiang],
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection,
ICCV17(202-211)
IEEE DOI 1802
convolution, data mining, feature extraction, image enhancement, image resolution, image segmentation, Visualization BibRef


Zhang, J.[Jing], Fan, D.P.[Deng-Ping], Dai, Y.[Yuchao], Yu, X.[Xin], Zhong, Y.[Yiran], Barnes, N.[Nick], Shao, L.[Ling],
RGB-D Saliency Detection via Cascaded Mutual Information Minimization,
ICCV21(4318-4327)
IEEE DOI 2203
Codes, Annotations, Computational modeling, Redundancy, Benchmark testing, Minimization, grouping and shape BibRef

Zhou, T.[Tao], Fu, H.Z.[Hua-Zhu], Chen, G.[Geng], Zhou, Y.[Yi], Fan, D.P.[Deng-Ping], Shao, L.[Ling],
Specificity-preserving RGB-D Saliency Detection,
ICCV21(4661-4671)
IEEE DOI 2203
Codes, Fuses, Computational modeling, Benchmark testing, Feature extraction, Light fields, BibRef

Wu, Z.W.[Zong-Wei], Allibert, G.[Guillaume], Stolz, C.[Christophe], Ma, C.[Chao], Demonceaux, C.[Cédric],
Modality-Guided Subnetwork for Salient Object Detection,
3DV21(515-524)
IEEE DOI 2201
Adaptation models, Solid modeling, Costs, Shape, Object detection, Transforms, RGBD Salient Object Detection, Cross Modal Fusion BibRef

Paigwar, A.[Anshul], Sierra-Gonzalez, D.[David], Erkent, Ö.[Özgür], Laugier, C.[Christian],
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR,
AVVision21(2926-2933)
IEEE DOI 2112
Location awareness, Laser radar, Runtime, Object detection, Feature extraction, Cameras BibRef

Ji, W.[Wei], Li, J.J.[Jing-Jing], Yu, S.[Shuang], Zhang, M.[Miao], Piao, Y.[Yongri], Yao, S.[Shunyu], Bi, Q.[Qi], Ma, K.[Kai], Zheng, Y.F.[Ye-Feng], Lu, H.C.[Hu-Chuan], Cheng, L.[Li],
Calibrated RGB-D Salient Object Detection,
CVPR21(9466-9476)
IEEE DOI 2111
Codes, Fuses, Computational modeling, Object detection, Boosting, Calibration BibRef

Sun, P.[Peng], Zhang, W.[Wenhu], Wang, H.Y.[Huan-Yu], Li, S.Y.[Song-Yuan], Li, X.[Xi],
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion,
CVPR21(1407-1417)
IEEE DOI 2111
Geometry, Visualization, Computer architecture, Object detection, Benchmark testing, Pattern recognition BibRef

Zhang, B.[Bin], Kang, X.[Xuejing], Ming, A.[Anlong],
BP-net: deep learning-based superpixel segmentation for RGB-D image,
ICPR21(7433-7438)
IEEE DOI 2105
Geometry, Image segmentation, Shape, Image edge detection, Neural networks, Filtering algorithms, Feature extraction BibRef

Wang, X.Q.[Xue-Qing], Hou, Y.L.[Ya-Li], Hao, X.L.[Xiao-Li], Shen, Y.[Yan], Liu, S.[Shuai],
Automatic 3d Object Detection from RGB-D Data Using PU-GAN,
ISVC20(II:742-752).
Springer DOI 2103
BibRef

Zhao, X.Q.[Xiao-Qi], Zhang, L.H.[Li-He], Pang, Y.W.[You-Wei], Lu, H.C.[Hu-Chuan], Zhang, L.[Lei],
A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection,
ECCV20(XXII:646-662).
Springer DOI 2011
BibRef

Chen, S.H.[Shu-Han], Fu, Y.[Yun],
Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection,
ECCV20(VIII:520-538).
Springer DOI 2011
BibRef

Zhang, M.[Miao], Fei, S.X.[Sun Xiao], Liu, J.[Jie], Xu, S.[Shuang], Piao, Y.[Yongri], Lu, H.C.[Hu-Chuan],
Asymmetric Two-stream Architecture for Accurate RGB-D Saliency Detection,
ECCV20(XXVIII:374-390).
Springer DOI 2011
BibRef

Zhao, J.X.[Jia-Xing], Cao, Y.[Yang], Fan, D.P.[Deng-Ping], Cheng, M.M.[Ming-Ming], Li, X.Y.[Xuan-Yi], Zhang, L.[Le],
Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection,
CVPR19(3922-3931).
IEEE DOI 2002
BibRef

Wang, S.T.[Song-Tao], Zhou, Z.[Zhen], Qu, H.B.[Han-Bing], Li, B.[Bin],
RGB-D saliency detection under Bayesian framework,
ICPR16(1881-1886)
IEEE DOI 1705
Computational modeling, Feature extraction, Image color analysis, Solid modeling, Visualization BibRef

Yun, J.S., Sim, J.Y.,
Supervoxel-based saliency detection for large-scale colored 3D point clouds,
ICIP16(4062-4066)
IEEE DOI 1610
Clustering algorithms BibRef

Dabala, L.[Lukasz], Rokita, P.[Przemyslaw],
Depth Guided Detection of Salient Objects,
ICCVG16(197-205).
Springer DOI 1611
BibRef

Mukherjee, S., Cheng, I., Basu, A.,
Highlighting objects of interest in an image by integrating saliency and depth,
ICIP16(6-10)
IEEE DOI 1610
Adaptation models BibRef

Xue, H.Y.[Hao-Yang], Gu, Y.[Yun], Li, Y.J.[Yi-Jun], Yang, J.[Jie],
RGB-D saliency detection via mutual guided manifold ranking,
ICIP15(666-670)
IEEE DOI 1512
Depth map cues; Mutual guided manifold ranking; Saliency detection BibRef

Ren, J.Q.[Jian-Qiang], Gong, X.J.[Xiao-Jin], Yu, L.[Lu], Zhou, W.H.[Wen-Hui], Yang, M.Y.[Michael Ying],
Exploiting global priors for RGB-D saliency detection,
FusionDynamic15(25-32)
IEEE DOI 1510
Image color analysis BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Point Cloud Processing for Neural Networks, Convolutional Neural Networks .


Last update:Jun 19, 2022 at 13:58:21