Luo, Y.[Ye],
Yuan, J.S.[Jun-Song],
Xue, P.[Ping],
Tian, Q.[Qi],
Saliency Density Maximization for Efficient Visual Objects Discovery,
CirSysVideo(21), No. 12, December 2011, pp. 1822-1834.
IEEE DOI
1112
BibRef
Earlier:
Saliency Density Maximization for Object Detection and Localization,
ACCV10(III: 396-408).
Springer DOI
1011
BibRef
Luo, Y.[Ye],
Zhao, G.Q.[Gang-Qiang],
Yuan, J.S.[Jun-Song],
Thematic Saliency Detection Using Spatial-Temporal Context,
LSVSM13(347-353)
IEEE DOI
1403
data mining
BibRef
Hu, K.T.[Kang-Ting],
Leou, J.J.[Jin-Jang],
Hsiao, H.H.[Han-Hui],
Spatiotemporal saliency detection and salient region determination
for H.264 videos,
JVCIR(24), No. 7, 2013, pp. 760-772.
Elsevier DOI
1309
BibRef
Earlier:
Visual attention region determination for H.264 videos,
ICPR12(2038-2041).
WWW Link.
1302
Spatial saliency
BibRef
Ren, Z.X.[Zhi-Xiang],
Gao, S.H.[Sheng-Hua],
Chia, L.T.[Liang-Tien],
Rajan, D.,
Regularized Feature Reconstruction for
Spatio-Temporal Saliency Detection,
IP(22), No. 8, 2013, pp. 3120-3132.
IEEE DOI
1307
Laplace transforms, video saliency detection
BibRef
Liu, Z.[Zhi],
Zou, W.B.[Wen-Bin],
Le Meur, O.[Olivier],
Saliency Tree: A Novel Saliency Detection Framework,
IP(23), No. 5, May 2014, pp. 1937-1952.
IEEE DOI
1405
image colour analysis
BibRef
Perrin, A.F.[Anne-Flore],
Zhang, L.[Lu],
Le Meur, O.[Olivier],
How Well Current Saliency Prediction Models Perform on UAVs Videos?,
CAIP19(I:311-323).
Springer DOI
1909
BibRef
Le Meur, O.[Olivier],
Robustness and repeatability of saliency models subjected to visual
degradations,
ICIP11(3285-3288).
IEEE DOI
1201
BibRef
Ren, Z.X.[Zhi-Xiang],
Gao, S.H.[Sheng-Hua],
Chia, L.T.[Liang-Tien],
Tsang, I.W.,
Region-Based Saliency Detection and Its Application in Object
Recognition,
CirSysVideo(24), No. 5, May 2014, pp. 769-779.
IEEE DOI
1405
Clustering algorithms
BibRef
Hu, Y.Q.[Yi-Qun],
Ren, Z.X.[Zhi-Xiang],
Rajan, D.[Deepu],
Chia, L.T.[Liang-Tien],
Salient Region Detection by Jointly Modeling Distinctness and
Redundancy of Image Content,
ACCV10(II: 515-526).
Springer DOI
1011
BibRef
Yi, Y.[Yang],
Ding, J.[Jia],
Lai, J.L.[Jie-Ling],
A novel video salient object extraction method based on visual
attention,
SP:IC(28), No. 1, January 2013, pp. 45-54.
Elsevier DOI
1301
Video object segmentation, Salient object extraction, Visual attention
model, Attention object growing
BibRef
Li, Y.,
Sheng, B.,
Ma, L.,
Wu, W.,
Xie, Z.,
Temporally Coherent Video Saliency Using Regional Dynamic Contrast,
CirSysVideo(23), No. 12, 2013, pp. 2067-2076.
IEEE DOI
1312
Computational efficiency.
Region-based visual dynamic contrast.
BibRef
Han, J.,
He, S.,
Qian, X.,
Wang, D.,
Guo, L.,
Liu, T.,
An Object-Oriented Visual Saliency Detection Framework Based on
Sparse Coding Representations,
CirSysVideo(23), No. 12, 2013, pp. 2009-2021.
IEEE DOI
1312
Computer vision
BibRef
Li, W.T.[Wei-Te],
Chang, H.S.[Haw-Shiuan],
Lien, K.C.[Kuo-Chin],
Chang, H.T.[Hui-Tang],
Wang, Y.F.,
Exploring Visual and Motion Saliency for
Automatic Video Object Extraction,
IP(22), No. 7, 2013, pp. 2600-2610.
IEEE DOI conditional random field, foreground object extraction;
foreground-background region separation, visual saliency
1307
BibRef
Li, W.T.[Wei-Te],
Chang, H.T.[Hui-Tang],
Lyu, H.S.[Hermes Shing],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Automatic saliency inspired foreground object extraction from videos,
ICIP12(1089-1092).
IEEE DOI
1302
BibRef
Yang, J.,
Zhao, G.,
Yuan, J.,
Shen, X.,
Lin, Z.,
Price, B.,
Brandt, J.,
Discovering Primary Objects in Videos by Saliency Fusion and
Iterative Appearance Estimation,
CirSysVideo(26), No. 6, June 2016, pp. 1070-1083.
IEEE DOI
1606
Estimation
BibRef
Bhattacharya, S.[Saumik],
Venkatsh, K.S.,
Gupta, S.[Sumana],
Background estimation and motion saliency detection using total
variation-based video decomposition,
SIViP(11), No. 1, January 2017, pp. 113-121.
WWW Link.
1702
BibRef
Earlier: A1, A3, A2:
Visual saliency detection using video decomposition,
ICIP16(684-688)
IEEE DOI
1610
Cameras
BibRef
Duval-Poo, M.A.,
Noceti, N.,
Odone, F.[Francesca],
de Vito, E.[Ernesto],
Scale Invariant and Noise Robust Interest Points With Shearlets,
IP(26), No. 6, June 2017, pp. 2853-2867.
IEEE DOI
1705
Gaussian noise, feature extraction, image coding, Gaussian noise,
blob detection, blob-like features, compression artifacts,
directional multiscale framework, image compression,
shearlets framework,
BibRef
Malafronte, D.[Damiano],
Odone, F.[Francesca],
de Vito, E.[Ernesto],
Detecting Spatio-Temporally Interest Points Using the Shearlet
Transform,
IbPRIA17(501-510).
Springer DOI
1706
BibRef
Li, J.,
Xia, C.,
Chen, X.,
A Benchmark Dataset and Saliency-Guided Stacked Autoencoders for
Video-Based Salient Object Detection,
IP(27), No. 1, January 2018, pp. 349-364.
IEEE DOI
1712
learning (artificial intelligence), object detection,
video coding, SOD dataset, benchmark dataset, eye-tracking data,
video dataset
BibRef
Sun, X.[Xiao],
Hu, Y.X.[Yu-Xing],
Zhang, L.M.[Lu-Ming],
Chen, Y.X.[Yan-Xiang],
Li, P.[Ping],
Xie, Z.[Zhao],
Liu, Z.G.[Zhen-Guang],
Camera-Assisted Video Saliency Prediction and Its Applications,
Cyber(48), No. 9, September 2018, pp. 2520-2530.
IEEE DOI
1809
Find important regions in the image.
cameras, feature extraction,
learning (artificial intelligence), matrix decomposition,
video saliency
BibRef
Zhang, P.,
Yan, P.,
Wu, J.,
Liu, J.,
Shen, F.,
Unsupervised Saliency Detection in 3-D-Video Based on Multiscale
Segmentation and Refinement,
SPLetters(25), No. 9, September 2018, pp. 1384-1388.
IEEE DOI
1809
feature extraction, graph theory, image colour analysis,
image fusion, image motion analysis, image segmentation,
segmentation
BibRef
Pang, Y.,
Ye, L.,
Li, X.,
Pan, J.,
Incremental Learning With Saliency Map for Moving Object Detection,
CirSysVideo(28), No. 3, March 2018, pp. 640-651.
IEEE DOI
1804
Gaussian processes, image motion analysis, image sequences,
learning (artificial intelligence), minimisation,
subspace analysis
BibRef
Xu, B.,
Niu, Y.,
Accurate Object Segmentation for Video Sequences via
Temporal-Spatial-Frequency Saliency Model,
IEEE_Int_Sys(33), No. 1, January 2018, pp. 18-28.
IEEE DOI
1805
Cameras, Frequency-domain analysis, Image color analysis,
Image segmentation, Object segmentation, Video sequences,
visualization
BibRef
Le, T.N.[Trung-Nghia],
Sugimoto, A.[Akihiro],
Video Salient Object Detection Using Spatiotemporal Deep Features,
IP(27), No. 10, October 2018, pp. 5002-5015.
IEEE DOI
1808
BibRef
Earlier:
Contrast Based Hierarchical Spatial-Temporal Saliency for Video,
PSIVT15(734-748).
Springer DOI
1602
feature extraction, image colour analysis, image representation,
image segmentation, object detection, random processes,
video object segmentation
BibRef
Chen, C.,
Li, S.,
Qin, H.,
Pan, Z.,
Yang, G.,
Bilevel Feature Learning for Video Saliency Detection,
MultMed(20), No. 12, December 2018, pp. 3324-3336.
IEEE DOI
1812
feature extraction, learning (artificial intelligence),
Markov processes, object detection, video signal processing,
Video Saliency Detection
BibRef
Chen, Y.[Yu],
Xiao, J.[Jing],
Hu, L.[Liuyi],
Chen, D.[Dan],
Wang, Z.Y.[Zhong-Yuan],
Li, D.S.[Deng-Shi],
Video Saliency Detection Using Spatiotemporal Cues,
IEICE(E101-D), No. 9, September 2018, pp. 2201-2208.
WWW Link.
1809
BibRef
Tang, Y.[Yi],
Zou, W.B.[Wen-Bin],
Jin, Z.[Zhi],
Chen, Y.H.[Yu-Huan],
Hua, Y.[Yang],
Li, X.[Xia],
Weakly Supervised Salient Object Detection With Spatiotemporal
Cascade Neural Networks,
CirSysVideo(29), No. 7, July 2019, pp. 1973-1984.
IEEE DOI
1907
Videos, Object detection, Spatiotemporal phenomena,
Neural networks, Saliency detection, Machine learning,
cascade fully convolutional network
BibRef
Peng, Q.,
Cheung, Y.,
Automatic Video Object Segmentation Based on Visual and Motion
Saliency,
MultMed(21), No. 12, December 2019, pp. 3083-3094.
IEEE DOI
1912
Manifolds, Visualization, Object segmentation, Motion segmentation,
Image segmentation, Autoregressive processes, Labeling,
graph model
BibRef
Wen, H.F.[Hong-Fa],
Zhou, X.F.[Xiao-Fei],
Sun, Y.Q.[Yao-Qi],
Zhang, J.Y.[Ji-Yong],
Yan, C.G.[Cheng-Gang],
Deep fusion based video saliency detection,
JVCIR(62), 2019, pp. 279-285.
Elsevier DOI
1908
Video saliency, Convolutional network, Feature integration, Boundary
BibRef
Patil, P.W.,
Murala, S.,
MSFgNet:
A Novel Compact End-to-End Deep Network for Moving Object Detection,
ITS(20), No. 11, November 2019, pp. 4066-4077.
IEEE DOI
1911
Videos, Object detection, Estimation, Feature extraction, Lighting,
Image color analysis, Saliency detection, Background estimation, CNN
BibRef
Patil, P.W.,
Thawakar, O.,
Dudhane, A.,
Murala, S.,
Motion Saliency Based Generative Adversarial Network for Underwater
Moving Object Segmentation,
ICIP19(1565-1569)
IEEE DOI
1910
Underwater medium, motion saliency,
Generative adversarial network, frame segmentation
BibRef
Maczyta, L.[Léo],
Bouthemy, P.[Patrick],
Meur, O.L.[Olivier Le],
CNN-based temporal detection of motion saliency in videos,
PRL(128), 2019, pp. 298-305.
Elsevier DOI
1912
Motion saliency, Temporal detection, CNN-based classification,
Dominant motion estimation, Video analysis
BibRef
Xu, M.,
Liu, B.,
Fu, P.,
Li, J.,
Hu, Y.H.,
Video Saliency Detection via Graph Clustering With Motion Energy and
Spatiotemporal Objectness,
MultMed(21), No. 11, November 2019, pp. 2790-2805.
IEEE DOI
1911
Spatiotemporal phenomena, Visualization, Dynamics, Proposals,
Feature extraction, Fuses, Video saliency,
graph clustering
BibRef
Chen, C.,
Wang, G.,
Peng, C.,
Zhang, X.,
Qin, H.,
Improved Robust Video Saliency Detection Based on Long-Term
Spatial-Temporal Information,
IP(29), No. 1, 2020, pp. 1090-1100.
IEEE DOI
1911
Saliency detection, Deep learning, Quality assessment, Trajectory,
Computational modeling, Color, Training, Video saliency detection,
long-term information revealing
BibRef
Lai, Q.,
Wang, W.,
Sun, H.,
Shen, J.,
Video Saliency Prediction Using Spatiotemporal Residual Attentive
Networks,
IP(29), No. 1, 2020, pp. 1113-1126.
IEEE DOI
1911
Visualization, Computational modeling, Spatiotemporal phenomena,
Dynamics, Task analysis, Data models, Predictive models,
video saliency
BibRef
Fang, Y.,
Ding, G.,
Li, J.,
Fang, Z.,
Deep3DSaliency: Deep Stereoscopic Video Saliency Detection Model by
3D Convolutional Networks,
IP(28), No. 5, May 2019, pp. 2305-2318.
IEEE DOI
1903
convolutional neural nets, deconvolution, feature extraction,
image sequences, object detection, optimisation,
3D convolutional neural networks
BibRef
Fang, Y.M.[Yu-Ming],
Zhang, X.Q.[Xiao-Qiang],
Yuan, F.N.[Fei-Niu],
Imamoglu, N.[Nevrez],
Liu, H.W.[Hai-Wen],
Video saliency detection by gestalt theory,
PR(96), 2019, pp. 106987.
Elsevier DOI
1909
Visual attention, Video saliency detection, Gestalt theory,
Uncertainty weighting, Spatiotemporal saliency
BibRef
Wang, Q.[Qiong],
Zhang, L.[Lu],
Zou, W.B.[Wen-Bin],
Kpalma, K.[Kidiyo],
Salient video object detection using a virtual border and guided
filter,
PR(97), 2020, pp. 106998.
Elsevier DOI
1910
Video salient object detection, Distance transform,
Guided filter, Global motion
BibRef
Wu, Z.,
Su, L.,
Huang, Q.,
Learning Coupled Convolutional Networks Fusion for Video Saliency
Prediction,
CirSysVideo(29), No. 10, October 2019, pp. 2960-2971.
IEEE DOI
1910
convolutional neural nets, feature extraction,
image motion analysis, inference mechanisms,
fully convolutional network
BibRef
Li, J.,
Fu, K.,
Zhao, S.,
Ge, S.,
Spatiotemporal Knowledge Distillation for Efficient Estimation of
Aerial Video Saliency,
IP(29), No. 1, 2020, pp. 1902-1914.
IEEE DOI
1912
Computational modeling, Redundancy, Estimation, Visualization,
Data models, Spatiotemporal phenomena, Drones,
aerial video
BibRef
Zhang, K.[Kao],
Chen, Z.Z.[Zhen-Zhong],
Video Saliency Prediction Based on Spatial-Temporal Two-Stream
Network,
CirSysVideo(29), No. 12, December 2019, pp. 3544-3557.
IEEE DOI
1912
Feature extraction, Predictive models, Streaming media,
Visualization, Spatiotemporal phenomena, Computational modeling,
deep learning
BibRef
Zhang, K.[Kao],
Chen, Z.Z.[Zhen-Zhong],
Liu, S.[Shan],
A Spatial-Temporal Recurrent Neural Network for Video Saliency
Prediction,
IP(30), 2021, pp. 572-587.
IEEE DOI
2012
Feature extraction, Predictive models, Computational modeling,
Visualization, Streaming media, Biological system modeling,
deep learning
BibRef
Kavak, Y.[Yasin],
Erdem, E.[Erkut],
Erdem, A.[Aykut],
Hedging static saliency models to predict dynamic saliency,
SP:IC(81), 2020, pp. 115694.
Elsevier DOI
1912
Dynamic saliency, Hedge algorithm,
Decision theoretic online learning, Feature integration
BibRef
Jiang, L.[Lai],
Xu, M.[Mai],
Zhang, S.[Shanyi],
Sigal, L.[Leonid],
DeepCT: A novel deep complex-valued network with learnable transform
for video saliency prediction,
PR(102), 2020, pp. 107234.
Elsevier DOI
2003
Saliency prediction, Complex-valued network,
Learnable transform, Convolutional LSTM
BibRef
Fang, Y.M.[Yu-Ming],
Zhang, C.[Chi],
Min, X.K.[Xiong-Kuo],
Huang, H.Q.[Han-Qin],
Yi, Y.G.[Yu-Gen],
Zhai, G.T.[Guang-Tao],
Lin, C.W.[Chia-Wen],
DevsNet: Deep Video Saliency Network using Short-term and Long-term
Cues,
PR(103), 2020, pp. 107294.
Elsevier DOI
2005
Video saliency detection, Spatiotemporal saliency,
3D convolution network (3D-ConvNet),
Bidirectional convolutional long-short term memory network (B-ConvLSTM)
BibRef
Li, Y.,
Li, S.,
Chen, C.,
Hao, A.,
Qin, H.,
Accurate and Robust Video Saliency Detection via Self-Paced Diffusion,
MultMed(22), No. 5, May 2020, pp. 1153-1167.
IEEE DOI
2005
Saliency detection, Proposals, Video sequences, Spatial coherence,
Computational modeling, Optical imaging, Optical sensors,
self-paced saliency diffusion
BibRef
Zhou, F.[Feng],
Shuai, H.[Hui],
Liu, Q.S.[Qing-Shan],
Guo, G.D.[Guo-Dong],
Flow driven attention network for video salient object detection,
IET-IPR(14), No. 6, 11 May 2020, pp. 997-1004.
DOI Link
2005
BibRef
Li, J.X.[Jun-Xia],
Pan, Z.F.[Ze-Feng],
Liu, Q.S.[Qing-Shan],
Cui, Y.[Ying],
Sun, Y.[Yubao],
Complementarity-Aware Attention Network for Salient Object Detection,
Cyber(52), No. 2, February 2022, pp. 873-886.
IEEE DOI
2202
Feature extraction, Saliency detection, Task analysis,
Visualization, Semantics, Object detection, Deep learning,
self-attention mechanism
BibRef
Wang, Q.[Qiong],
Zhang, L.[Lu],
Li, Y.[Yan],
Kpalma, K.[Kidiyo],
Overview of deep-learning based methods for salient object detection
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PR(104), 2020, pp. 107340.
Elsevier DOI
2005
Deep-learning, Salient object detection, Video
BibRef
Shokri, M.[Mohammad],
Harati, A.[Ahad],
Taba, K.[Kimya],
Salient object detection in video using deep non-local neural
networks,
JVCIR(68), 2020, pp. 102769.
Elsevier DOI
2005
Video saliency detection, Deep learning,
Non-local neural networks, Fully convolutional neural networks
BibRef
Xu, M.,
Liu, B.,
Fu, P.,
Li, J.,
Hu, Y.H.,
Feng, S.,
Video Salient Object Detection via Robust Seeds Extraction and
Multi-Graphs Manifold Propagation,
CirSysVideo(30), No. 7, July 2020, pp. 2191-2206.
IEEE DOI
2007
Spatiotemporal phenomena, Dynamics, Manifolds, Object detection,
Reliability, Visualization, Feature extraction, Video saliency,
multi-graphs saliency propagation
BibRef
Chen, J.[Jin],
Song, H.H.[Hui-Hui],
Zhang, K.H.[Kai-Hua],
Liu, B.[Bo],
Liu, Q.S.[Qing-Shan],
Video saliency prediction using enhanced spatiotemporal alignment
network,
PR(109), 2021, pp. 107615.
Elsevier DOI
2009
Video saliency prediction, Feature alignment,
Deformable convolution, Bidirectional ConvLSTM
BibRef
Wang, B.,
Liu, W.,
Han, G.,
He, S.,
Learning Long-Term Structural Dependencies for Video Salient Object
Detection,
IP(29), 2020, pp. 9017-9031.
IEEE DOI
2009
Predictive models, Object detection, Feature extraction,
Saliency detection, Convolution, Merging, Object recognition,
supervoxel
BibRef
Wang, W.G.[Wen-Guan],
Shen, J.B.[Jian-Bing],
Xie, J.W.[Jian-Wen],
Cheng, M.M.[Ming-Ming],
Ling, H.B.[Hai-Bin],
Borji, A.[Ali],
Revisiting Video Saliency Prediction in the Deep Learning Era,
PAMI(43), No. 1, January 2021, pp. 220-237.
IEEE DOI
2012
Visualization, Benchmark testing, Sports, Predictive models,
Analytical models, Computational modeling, Task analysis,
deep learning
BibRef
Guo, F.,
Wang, W.,
Shen, Z.,
Shen, J.,
Shao, L.,
Tao, D.,
Motion-Aware Rapid Video Saliency Detection,
CirSysVideo(30), No. 12, December 2020, pp. 4887-4898.
IEEE DOI
2012
Saliency detection, Video sequences, Spatiotemporal phenomena,
Object detection, Dynamics, Optical propagation, Optical imaging,
spatiotemporal salient object detection
BibRef
Tang, J.[Jin],
Fan, D.Z.[Dong-Zhe],
Wang, X.X.[Xiao-Xiao],
Tu, Z.Z.[Zheng-Zheng],
Li, C.L.[Cheng-Long],
RGBT Salient Object Detection:
Benchmark and A Novel Cooperative Ranking Approach,
CirSysVideo(30), No. 12, December 2020, pp. 4421-4433.
IEEE DOI
2012
Saliency detection, Object detection, Computational modeling,
Task analysis, Lighting, Collaboration, Benchmark testing, joint optimization
BibRef
Qiao, M.,
Xu, M.,
Wang, Z.,
Borji, A.,
Viewport-Dependent Saliency Prediction in 360° Video,
MultMed(23), 2021, pp. 748-760.
IEEE DOI
2102
Databases, Visualization, Task analysis,
Predictive models, Heating systems, Observers, 360° video, viewport,
multi-task DNN
BibRef
Li, Y.X.[Yun-Xiao],
Li, S.[Shuai],
Chen, C.Z.[Chengli-Zhao],
Hao, A.[Aimin],
Qin, H.[Hong],
A Plug-and-Play Scheme to Adapt Image Saliency Deep Model for Video
Data,
CirSysVideo(31), No. 6, June 2021, pp. 2315-2327.
IEEE DOI
2106
Saliency detection, Solid modeling, Spatiotemporal phenomena,
Feature extraction, Image color analysis, Network architecture,
weakly supervised learning
BibRef
Zou, W.B.[Wen-Bin],
Zhuo, S.K.[Sheng-Kai],
Tang, Y.[Yi],
Tian, S.S.[Shi-Shun],
Li, X.[Xia],
Xu, C.[Chen],
STA3D: Spatiotemporally attentive 3D network for video saliency
prediction,
PRL(147), 2021, pp. 78-84.
Elsevier DOI
2106
Saliency prediction, Video, 3D networks, Deep learning
BibRef
Wang, W.[Wenguan],
Shen, J.B.[Jian-Bing],
Lu, X.[Xiankai],
Hoi, S.C.H.[Steven C. H.],
Ling, H.B.[Hai-Bin],
Paying Attention to Video Object Pattern Understanding,
PAMI(43), No. 7, July 2021, pp. 2413-2428.
IEEE DOI
2106
Visualization, Object segmentation, Motion segmentation,
Task analysis, Annotations, Biological system modeling,
video salient object detection
BibRef
Yin, H.[Hui],
Chen, N.[Ning],
Yang, L.[Lin],
Wan, J.[Jin],
Pop-net:
A self-growth network for popping out the salient object in videos,
IET-CV(15), No. 5, 2021, pp. 334-345.
DOI Link
2107
BibRef
Wang, Y.H.[Yu-Hao],
Liu, Z.R.[Zhuo-Ran],
Xia, Y.B.[Yi-Bo],
Zhu, C.B.[Chun-Bo],
Zhao, D.P.[Dan-Pei],
Spatiotemporal module for video saliency prediction based on
self-attention,
IVC(112), 2021, pp. 104216.
Elsevier DOI
2107
Video saliency prediction, Spatio-temporal, Self-attention, Convolutional LSTM
BibRef
Bellitto, G.,
Proietto Salanitri, F.,
Palazzo, S.,
Rundo, F.,
Giordano, D.[Daniela],
Spampinato, C.[Concetto],
Hierarchical Domain-Adapted Feature Learning for Video Saliency
Prediction,
IJCV(129), No. 12, December 2021, pp. 3216-3232.
Springer DOI
2111
BibRef
Huang, L.M.[Li-Ming],
Song, K.[Kechen],
Wang, J.[Jie],
Niu, M.H.[Meng-Hui],
Yan, Y.H.[Yun-Hui],
Multi-Graph Fusion and Learning for RGBT Image Saliency Detection,
CirSysVideo(32), No. 3, March 2022, pp. 1366-1377.
IEEE DOI
2203
Saliency detection, Feature extraction, Manifolds, Imaging,
Adaptation models, Training, Object detection,
adaptive ranking
BibRef
Chen, C.L.[Cheng-Lizhao],
Song, J.[Jia],
Peng, C.[Chong],
Wang, G.D.[Guo-Dong],
Fang, Y.M.[Yu-Ming],
A Novel Video Salient Object Detection Method via Semisupervised
Motion Quality Perception,
CirSysVideo(32), No. 5, May 2022, pp. 2732-2745.
IEEE DOI
2205
Streaming media, Object detection, Testing,
Spatiotemporal phenomena, Feature extraction, Data models,
semisupervised learning
BibRef
Wang, J.[Jie],
Song, K.C.[Ke-Chen],
Bao, Y.Q.[Yan-Qi],
Huang, L.M.[Li-Ming],
Yan, Y.H.[Yun-Hui],
CGFNet: Cross-Guided Fusion Network for RGB-T Salient Object
Detection,
CirSysVideo(32), No. 5, May 2022, pp. 2949-2961.
IEEE DOI
2205
Feature extraction, Decoding, Object detection, Semantics,
Image edge detection, Task analysis, Image segmentation,
cross-level enhancement
BibRef
Huo, F.S.[Fu-Shuo],
Zhu, X.G.[Xue-Gui],
Zhang, L.[Lei],
Liu, Q.F.[Qi-Feng],
Shu, Y.[Yu],
Efficient Context-Guided Stacked Refinement Network for RGB-T Salient
Object Detection,
CirSysVideo(32), No. 5, May 2022, pp. 3111-3124.
IEEE DOI
2205
Feature extraction, Task analysis, Fuses, Object detection,
Image segmentation, Semantics, Lighting, Salient object detection,
information fusion
BibRef
Tu, Z.Z.[Zheng-Zheng],
Li, Z.[Zhun],
Li, C.L.[Cheng-Long],
Tang, J.[Jin],
Weakly Alignment-Free RGBT Salient Object Detection With Deep
Correlation Network,
IP(31), 2022, pp. 3752-3764.
IEEE DOI
2206
Correlation, Convolution, Decoding, Semantics, Feature extraction,
Kernel, Task analysis, Salient object detection (SOD),
bi-directional decoder
BibRef
Sun, F.[Fan],
Zhou, W.[Wujie],
Ye, L.[Lv],
Yu, L.[Lu],
Hierarchical Decoding Network Based on Swin Transformer for Detecting
Salient Objects in RGB-T Images,
SPLetters(29), 2022, pp. 1714-1718.
IEEE DOI
2208
Feature extraction, Semantics, Decoding, Transformers, Convolution,
Training, Image segmentation, Transformer, hierarchical decoder,
global saliency per- ception
BibRef
Min, D.Y.[Ding-Yao],
Zhang, C.[Chao],
Lu, Y.K.[Yu-Kang],
Fu, K.[Keren],
Zhao, Q.J.[Qi-Jun],
Mutual-Guidance Transformer-Embedding Network for Video Salient
Object Detection,
SPLetters(29), 2022, pp. 1674-1678.
IEEE DOI
2208
Transformers, Feature extraction, Radio frequency,
Object detection, Task analysis, Standards, Optical imaging,
video salient object detection
BibRef
Lu, Y.K.[Yu-Kang],
Min, D.Y.[Ding-Yao],
Fu, K.[Keren],
Zhao, Q.J.[Qi-Jun],
Depth-Cooperated Trimodal Network for Video Salient Object Detection,
ICIP22(116-120)
IEEE DOI
2211
Object detection, Benchmark testing, Spatiotemporal phenomena,
Optical flow, Video salient object detection, multimodal, depth,
attention
BibRef
Zhang, Q.[Qiang],
Xi, R.[Ruida],
Xiao, T.L.[Tong-Lin],
Huang, N.C.[Nian-Chang],
Luo, Y.J.[Yong-Jiang],
Enabling modality interactions for RGB-T salient object detection,
CVIU(222), 2022, pp. 103514.
Elsevier DOI
2209
RGB-T salient object detection, Modality interactions, Scale interactions
BibRef
Chen, G.[Gang],
Shao, F.[Feng],
Chai, X.L.[Xiong-Li],
Chen, H.W.[Hang-Wei],
Jiang, Q.P.[Qiu-Ping],
Meng, X.C.[Xiang-Chao],
Ho, Y.S.[Yo-Sung],
CGMDRNet: Cross-Guided Modality Difference Reduction Network for
RGB-T Salient Object Detection,
CirSysVideo(32), No. 9, September 2022, pp. 6308-6323.
IEEE DOI
2209
Feature extraction, Image edge detection, Task analysis,
Object detection, Transformers, Semantics, Visualization,
transformer
BibRef
Xu, Y.[Yanyu],
Zhang, Z.H.[Zi-Heng],
Gao, S.H.[Sheng-Hua],
Spherical DNNs and Their Applications in 360° Images and Videos,
PAMI(44), No. 10, October 2022, pp. 7235-7252.
IEEE DOI
2209
Videos, Saliency detection, Distortion, Convolution, Task analysis,
Feature extraction, Kernel, Spherical deep neural networks,
360° videos
BibRef
Ma, C.[Cheng],
Sun, H.[Haowen],
Rao, Y.M.[Yong-Ming],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
Video Saliency Forecasting Transformer,
CirSysVideo(32), No. 10, October 2022, pp. 6850-6862.
IEEE DOI
2210
Forecasting, Transformers, Task analysis, Visualization,
Predictive models, Correlation, Video saliency forecasting, transformer
BibRef
Chen, C.Z.[Chengli-Zhao],
Wang, H.[Hengsen],
Fang, Y.M.[Yu-Ming],
Peng, C.[Chong],
A Novel Long-Term Iterative Mining Scheme for Video Salient Object
Detection,
CirSysVideo(32), No. 11, November 2022, pp. 7662-7676.
IEEE DOI
2211
Proposals, Task analysis, Spatiotemporal phenomena,
Object detection, Biological system modeling,
video salient object detection
BibRef
Pang, Y.[Yu],
Wu, H.[Hao],
Wu, C.D.[Cheng-Dong],
Cross-modal co-feedback cellular automata for RGB-T saliency
detection,
PR(135), 2023, pp. 109138.
Elsevier DOI
2212
RGB-T saliency detection, Cellular automata,
Cross-modal co-feedback framework, Pixel-wise refinement
BibRef
Huang, X.M.[Xiao-Ming],
Zhang, Y.J.[Yu-Jin],
Fast Video Saliency Detection via Maximally Stable Region Motion and
Object Repeatability,
MultMed(24), 2022, pp. 4458-4470.
IEEE DOI
2212
Object detection, Saliency detection, Image color analysis,
Feature extraction, Computational modeling, Hardware, Estimation,
object repeatability
BibRef
Chen, Y.W.[Yi-Wen],
Jin, X.J.[Xiao-Jie],
Shen, X.H.[Xiao-Hui],
Yang, M.H.[Ming-Hsuan],
Video Salient Object Detection via Contrastive Features and Attention
Modules,
WACV22(536-545)
IEEE DOI
2202
Runtime, Recurrent neural networks, Correlation,
Computational modeling, Semantics, Object detection, Segmentation,
Grouping and Shape
BibRef
Jiao, Y.X.[Ying-Xia],
Wang, X.[Xiao],
Chou, Y.C.[Yu-Cheng],
Yang, S.Y.[Shou-Yuan],
Ji, G.P.[Ge-Peng],
Zhu, R.[Rong],
Gao, G.[Ge],
Guidance and Teaching Network for Video Salient Object Detection,
ICIP21(2199-2203)
IEEE DOI
2201
Bridges, Image processing, Education, Modulation,
Graphics processing units, Object detection,
Teacher-student learning.
BibRef
Yang, J.W.[Jing-Wen],
Zhang, G.[Guanwen],
Yan, J.[JiaMing],
Zhou, W.[Wei],
TSMSAN: A Three-Stream Multi-Scale Attentive Network for Video
Saliency Detection,
ICPR21(4371-4376)
IEEE DOI
2105
Dynamics, Feature extraction, Spatiotemporal phenomena,
Task analysis, Saliency detection
BibRef
Yan, H.[Hang],
Xu, Y.L.[Yi-Ling],
Sun, J.[Jun],
Yang, L.[Le],
Zhang, Y.F.[Yun-Fei],
Huang, W.[Wei],
Fast Video Saliency Detection based on Feature Competition,
VCIP20(74-77)
IEEE DOI
2102
Feature extraction, Predictive models, Correlation, Visualization,
Task analysis, Adaptation models, Fuses, Video saliency,
ablation studies
BibRef
Tangemann, M.[Matthias],
Kümmerer, M.[Matthias],
Wallis, T.S.A.[Thomas S. A.],
Bethge, M.[Matthias],
Measuring the Importance of Temporal Features in Video Saliency,
ECCV20(XXVIII:667-684). xo
Springer DOI
2011
BibRef
Ren, S.C.[Su-Cheng],
Han, C.[Chu],
Yang, X.[Xin],
Han, G.Q.[Guo-Qiang],
He, S.F.[Sheng-Feng],
Tenet: Triple Excitation Network for Video Salient Object Detection,
ECCV20(V:212-228).
Springer DOI
2011
BibRef
Droste, R.[Richard],
Jiao, J.B.[Jian-Bo],
Noble, J.A.[J. Alison],
Unified Image and Video Saliency Modeling,
ECCV20(V:419-435).
Springer DOI
2011
BibRef
Nekrasov, V.,
Chen, H.,
Shen, C.,
Reid, I.D.,
Architecture Search of Dynamic Cells for Semantic Video Segmentation,
WACV20(1959-1968)
IEEE DOI
2006
Semantics, Optical imaging, Decoding,
Image segmentation, Optical propagation, Biomedical optical imaging
BibRef
Yan, P.X.[Peng-Xiang],
Li, G.B.[Guan-Bin],
Xie, Y.[Yuan],
Li, Z.[Zhen],
Wang, C.[Chuan],
Chen, T.S.[Tian-Shui],
Lin, L.[Liang],
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels,
ICCV19(7283-7292)
IEEE DOI
2004
convolutional neural nets, image colour analysis,
image enhancement, image motion analysis, image sequences, Coherence
BibRef
Fan, D.P.[Deng-Ping],
Wang, W.[Wenguan],
Cheng, M.M.[Ming-Ming],
Shen, J.B.[Jian-Bing],
Shifting More Attention to Video Salient Object Detection,
CVPR19(8546-8556).
IEEE DOI
2002
BibRef
Song, H.M.[Hong-Mei],
Wang, W.[Wenguan],
Zhao, S.[Sanyuan],
Shen, J.B.[Jian-Bing],
Lam, K.M.[Kin-Man],
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection,
ECCV18(XI: 744-760).
Springer DOI
1810
BibRef
Hoo, W.L.,
Chan, C.S.,
Anisotropic Partial Differential Equation Based Video Saliency
Detection,
ICIP18(2311-2315)
IEEE DOI
1809
Saliency detection, Image color analysis, Tensile stress,
Feature extraction, Task analysis, Discrete cosine transforms,
partial differential equation
BibRef
Xiao, B.[Bo],
Wang, B.[Bin],
Efficient HD video and image salient object detection with
hierarchical boolean map approach,
ICIVC17(1-7)
IEEE DOI
1708
Acceleration, Algorithm design and analysis, Feature extraction,
Floods, Image resolution, Object detection, Visualization,
hierarchical boolean map, parallel processing,
salient object detection, video consistency
BibRef
Zhang, L.[Long],
Liu, Y.J.[Yu-Jun],
Han, S.D.[Shou-Dong],
Video segmentation based on strong target constrained video saliency,
ICIVC17(356-360)
IEEE DOI
1708
Adaptive optics, Image color analysis, Image segmentation,
Optical imaging, Robustness, Target tracking,
full-connected conditional random field, object proposal,
super-pixel, video saliency, video, segmentation
BibRef
Wei, L.[Lina],
Wang, F.F.[Fang-Fang],
Li, X.[Xi],
Wu, F.[Fei],
Xiao, J.[Jun],
Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency
Detection,
ICIP17(4197-4201)
IEEE DOI
1803
Atomic layer deposition, Context modeling, Manifolds,
Saliency detection, Spatiotemporal phenomena,
Video saliency
BibRef
Hu, Y.T.[Yuan-Ting],
Huang, J.B.[Jia-Bin],
Schwing, A.G.[Alexander G.],
VideoMatch: Matching Based Video Object Segmentation,
ECCV18(VIII: 56-73).
Springer DOI
1810
BibRef
And:
Unsupervised Video Object Segmentation Using Motion Saliency-Guided
Spatio-Temporal Propagation,
ECCV18(I: 813-830).
Springer DOI
1810
BibRef
Chan, K.L.,
Saliency/non-saliency segregation in video sequences using
perception-based local ternary pattern features,
MVA17(510-513)
DOI Link
1708
Adaptation models, Color, Computational modeling, History,
Image color analysis, Mathematical model, Video sequences
BibRef
Karimi, A.H.,
Shafiee, M.J.,
Scharfenberger, C.,
Ben Daya, I.,
Haider, S.,
Talukdar, N.,
Clausi, D.A.,
Wong, A.,
Spatio-temporal saliency detection using abstracted fully-connected
graphical models,
ICIP16(694-698)
IEEE DOI
1610
Computational complexity
BibRef
Xue, K.[Kang],
Wang, X.[Xiying],
Ma, G.Y.[Geng-Yu],
Wang, H.T.[Hai-Tao],
Nam, D.[Dong_Kyung],
A video saliency detection method based on spatial and motion
information,
ICIP15(412-416)
IEEE DOI
1512
Classification, Motion, Saliency, Video
BibRef
Lee, T.[Teahyung],
Hwangbo, M.[Myung],
Alan, T.[Tanfer],
Tickoo, O.[Omesh],
Iyer, R.[Ravishankar],
Low-complexity HOG for efficient video saliency,
ICIP15(3749-3752)
IEEE DOI
1512
low-complexity HOG, video saliency
BibRef
Mansour, H.[Hassan],
Rane, S.[Shantanu],
Boufounos, P.T.[Petros T.],
Vetro, A.[Anthony],
Video querying via compact descriptors of visually salient objects,
ICIP14(2789-2793)
IEEE DOI
1502
Accuracy
BibRef
Hiratani, A.,
Nakashima, R.,
Matsumiya, K.,
Kuriki, I.,
Shioiri, S.,
Considerations of Self-Motion in Motion Saliency,
ACPR13(783-787)
IEEE DOI
1408
feature extraction
BibRef
Agarwal, D.,
Soni, N.,
Namboodiri, A.M.,
Salient object detection in SfM point cloud,
NCVPRIPG13(1-4)
IEEE DOI
1408
image motion analysis
BibRef
Gan, C.[Chuang],
Qin, Z.C.[Zeng-Chang],
Xu, J.[Jia],
Wan, T.[Tao],
Salient object detection in image sequences via spatial-temporal cue,
VCIP13(1-6)
IEEE DOI
1402
feature extraction
BibRef
Xia, Y.[Yang],
Hu, R.M.[Rui-Min],
Huang, Z.K.[Zhen-Kun],
Su, Y.[Yin],
A novel method for generation of motion saliency,
ICIP10(4685-4688).
IEEE DOI
1009
BibRef
Taylor, G.W.[Graham W.],
Fergus, R.[Rob],
Le Cun, Y.L.[Yann L.],
Bregler, C.[Christoph],
Convolutional Learning of Spatio-temporal Features,
ECCV10(VI: 140-153).
Springer DOI
1009
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Interactive Motion Segmentation .