Sompolinsky, H.,
Golomb, D., and
Kleinfeld, D.,
Global Processing of Visual Stimuli in a Neural Network
of Coupled Oscillators,
NAS(87), September 1990, pp. 7200-7204.
BibRef
9009
Mordohai, P.[Philippos],
Medioni, G.[Gérard],
Tensor Voting:
A Perceptual Organization Approach to Computer Vision and Machine Learning,
Morgan Claypool2006.
Synthesis Lectures on Image, Video, and Multimedia Processing
WWW Link.
Survey, Tensor Voting.
Tensor Voting.
BibRef
0600
Mordohai, P.[Philippos],
Medioni, G.[Gérard],
The Tensor Voting Framework,
ETCV04(Chapter 5).
BibRef
0400
Medioni, G.,
Lee, M.S.[Mi-Suen],
Tang, C.K.[Chi-Keung],
A Computational Framework for Segmentation and Grouping,
Elsevier2000.
ISBN: 0-444-50353-6
BibRef
0001
USC Computer Vision
Tensor Voting. Conceptual framework that solves a wide variety of problems -- Tensor Voting.
WWW Link.
BibRef
Shafiee, M.J.,
Siva, P.,
Scharfenberger, C.,
Fieguth, P.,
Wong, A.,
NeRD: A Neural Response Divergence Approach to Visual Saliency
Detection,
SPLetters(23), No. 10, October 2016, pp. 1404-1408.
IEEE DOI
1610
computer vision
BibRef
Shafiee, M.J.,
Siva, P.,
Fieguth, P.,
Wong, A.,
Embedded Motion Detection via Neural Response Mixture Background
Modeling,
ECVW16(837-844)
IEEE DOI
1612
BibRef
You, J.[Jia],
Zhang, L.[Lihe],
Qi, J.Q.[Jin-Qing],
Lu, H.C.[Hu-Chuan],
Salient object detection via point-to-set metric learning,
PRL(84), No. 1, 2016, pp. 85-90.
Elsevier DOI
1612
Salient object detection
BibRef
Ji, W.[Wei],
Li, J.J.[Jing-Jing],
Zhang, M.[Miao],
Piao, Y.R.[Yong-Ri],
Lu, H.C.[Hu-Chuan],
Accurate Rgb-d Salient Object Detection via Collaborative Learning,
ECCV20(XVIII:52-69).
Springer DOI
2012
BibRef
Tong, N.[Na],
Lu, H.C.[Hu-Chuan],
Ruan, X.[Xiang],
Yang, M.H.[Ming-Hsuan],
Salient object detection via bootstrap learning,
CVPR15(1884-1892)
IEEE DOI
1510
BibRef
Stefic, D.[Daria],
Patras, I.[Ioannis],
Action recognition using saliency learned from recorded human gaze,
IVC(52), No. 1, 2016, pp. 195-205.
Elsevier DOI
1609
BibRef
Earlier:
Learning visual saliency using topographic independent component
analysis,
ICIP14(1130-1134)
IEEE DOI
1502
Action recognition.
Face
BibRef
Huang, F.,
Qi, J.,
Lu, H.,
Zhang, L.,
Ruan, X.,
Salient Object Detection via Multiple Instance Learning,
IP(26), No. 4, April 2017, pp. 1911-1922.
IEEE DOI
1704
Computational modeling
BibRef
Zhang, L.[Lihe],
Ai, J.,
Jiang, B.[Bowen],
Lu, H.C.[Hu-Chuan],
Li, X.,
Saliency Detection via Absorbing Markov Chain With Learnt Transition
Probability,
IP(27), No. 2, February 2018, pp. 987-998.
IEEE DOI
1712
Computational modeling, Feature extraction, Image segmentation,
Markov processes, Object detection, Sparse matrices,
transition probability matrix
BibRef
Jiang, B.[Bowen],
Zhang, L.[Lihe],
Lu, H.C.[Hu-Chuan],
Yang, C.[Chuan],
Yang, M.H.[Ming-Hsuan],
Saliency Detection via Absorbing Markov Chain,
ICCV13(1665-1672)
IEEE DOI
1403
absorbing Markov chain, object detection, saliency detection
BibRef
Yang, C.[Chuan],
Zhang, L.[Lihe],
Lu, H.C.[Hu-Chuan],
Ruan, X.[Xiang],
Yang, M.H.[Ming-Hsuan],
Saliency Detection via Graph-Based Manifold Ranking,
CVPR13(3166-3173)
IEEE DOI
1309
BibRef
Zhang, C.[Chao],
Li, X.[Xiong],
Ruan, X.[Xiang],
Zhao, Y.M.[Yu-Ming],
Yang, M.H.[Ming-Hsuan],
Discriminative Generative Contour Detection,
BMVC13(xx-yy).
DOI Link
1402
BibRef
And: A1, A3, A4, A5, Only:
Contour detection via random forest,
ICPR12(2772-2775).
WWW Link.
1302
BibRef
Li, S.[Shuang],
Lu, H.C.[Hu-Chuan],
Lin, Z.[Zhe],
Shen, X.H.[Xiao-Hui],
Price, B.,
Adaptive Metric Learning for Saliency Detection,
IP(24), No. 11, November 2015, pp. 3321-3331.
IEEE DOI
1509
image coding
BibRef
Deutsch, S.[Shay],
Medioni, G.[Gérard],
Learning the Geometric Structure of Manifolds with Singularities Using
the Tensor Voting Graph,
JMIV(57), No. 3, March 2017, pp. 402-422.
WWW Link.
1702
BibRef
Earlier:
Unsupervised Learning Using the Tensor Voting Graph,
SSVM15(282-293).
Springer DOI
1506
BibRef
Fu, K.[Keren],
Gu, I.Y.H.[Irene Yu-Hua],
Yang, J.[Jie],
Saliency Detection by Fully Learning a Continuous Conditional Random
Field,
MultMed(19), No. 7, July 2017, pp. 1531-1544.
IEEE DOI
1706
Boosting, Estimation, Feature extraction,
Image segmentation, Labeling, Object detection,
Continuous conditional random field (C-CRF),
feature integration, learning, saliency map,
salient object detection, spatial, ranges
BibRef
Zeng, Y.,
Feng, M.,
Lu, H.,
Yang, G.,
Borji, A.,
An Unsupervised Game-Theoretic Approach to Saliency Detection,
IP(27), No. 9, September 2018, pp. 4545-4554.
IEEE DOI
1807
game theory, learning (artificial intelligence),
object detection, Saliency Game, complementary features,
visual attention
BibRef
Murabito, F.[Francesca],
Spampinato, C.[Concetto],
Palazzo, S.[Simone],
Giordano, D.[Daniela],
Pogorelov, K.[Konstantin],
Riegler, M.[Michael],
Top-down saliency detection driven by visual classification,
CVIU(172), 2018, pp. 67-76.
Elsevier DOI
1812
Visual attention, Image classification, Fully convolutional neural networks
BibRef
Palazzo, S.[Simone],
Rundo, F.[Francesco],
Battiato, S.[Sebastiano],
Giordano, D.[Daniela],
Spampinato, C.[Concetto],
Visual Saliency Detection guided by Neural Signals,
FG20(525-531)
IEEE DOI
2102
Visualization, Saliency detection, Electroencephalography,
Task analysis, Training, Brain modeling, Computational modeling
BibRef
Zhu, D.D.[Dan-Dan],
Luo, Y.[Ye],
Dai, L.[Lei],
Shao, X.[Xuan],
Zhou, Q.Q.[Qiang-Qiang],
Itti, L.[Laurent],
Lu, J.W.[Jian-Wei],
Salient object detection via a local and global method based on deep
residual network,
JVCIR(54), 2018, pp. 1-9.
Elsevier DOI
1806
BibRef
Earlier: A1, A2, A4, A6, A7, Only:
Saliency prediction based on new deep multi-layer convolution neural
network,
ICIP17(2711-2715)
IEEE DOI
1803
Salient object detection, Deep residual network, Local and global features.
Computational modeling, Convolution, Feature extraction,
Saliency detection, Support vector machines, Training.
BibRef
Bak, C.,
Kocak, A.[Aysun],
Erdem, E.[Erkut],
Erdem, A.[Aykut],
Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction,
MultMed(20), No. 7, July 2018, pp. 1688-1698.
IEEE DOI
1806
Computational modeling, Dynamics, Feature extraction,
Predictive models, Videos, Visualization, Dynamic saliency, deep learning
BibRef
Kocak, A.[Aysun],
Cizmeciler, K.[Kemal],
Erdem, A.[Aykut],
Erdem, E.[Erkut],
Top down saliency estimation via superpixel-based discriminative
dictionaries,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Yuan, Y.C.[Yu-Chen],
Li, C.Y.[Chang-Yang],
Kim, J.,
Cai, W.D.[Wei-Dong],
Feng, D.D.[David Dagan],
Dense and Sparse Labeling With Multidimensional Features for Saliency
Detection,
CirSysVideo(28), No. 5, May 2018, pp. 1130-1143.
IEEE DOI
1805
Channel estimation, DSL, Estimation, Feature extraction,
Image color analysis, Labeling, Neural networks,
sparse labeling (SL)
BibRef
Yuan, Y.C.[Yu-Chen],
Li, C.Y.[Chang-Yang],
Kim, J.,
Cai, W.D.[Wei-Dong],
Feng, D.D.[David Dagan],
Reversion Correction and Regularized Random Walk Ranking for Saliency
Detection,
IP(27), No. 3, March 2018, pp. 1311-1322.
IEEE DOI
1801
Estimation, Feature extraction, Image color analysis,
Image segmentation, Manifolds, Robustness, Visualization,
saliency optimization
BibRef
Li, C.Y.[Chang-Yang],
Yuan, Y.C.[Yu-Chen],
Cai, W.D.[Wei-Dong],
Xia, Y.[Yong],
Feng, D.D.[David Dagan],
Robust saliency detection via regularized random walks ranking,
CVPR15(2710-2717)
IEEE DOI
1510
BibRef
Monroy, R.[Rafael],
Lutz, S.[Sebastian],
Chalasani, T.[Tejo],
Smolic, A.[Aljosa],
SalNet360: Saliency maps for omni-directional images with CNN,
SP:IC(69), 2018, pp. 26-34.
Elsevier DOI
1811
Saliency, Omnidirectional Image (ODI),
Convolutional Neural Network (CNN), Virtual Reality (VR)
BibRef
Nguyen, A.,
Kim, J.,
Oh, H.,
Kim, H.,
Lin, W.,
Lee, S.,
Deep Visual Saliency on Stereoscopic Images,
IP(28), No. 4, April 2019, pp. 1939-1953.
IEEE DOI
1901
convolution, feature extraction, feedforward neural nets,
image restoration, learning (artificial intelligence),
deep learning
BibRef
Chen, J.Z.[Jia-Zhong],
Li, Q.Q.[Qing-Qing],
Li, P.[Ping],
Han, Y.[Yu],
Wu, L.[Lei],
Ling, H.F.[He-Fei],
Wu, W.M.[Wei-Min],
Saliency prediction by Mahalanobis distance of topological feature on
deep color components,
JVCIR(60), 2019, pp. 149-157.
Elsevier DOI
1903
Saliency, Deep color components, Topological feature,
Covariance matrix, Mahalanobis distance
BibRef
Wang, X.[Xiao],
Sun, T.[Tao],
Yang, R.[Rui],
Li, C.L.[Cheng-Long],
Luo, B.[Bin],
Tang, J.[Jin],
Quality-aware dual-modal saliency detection via deep reinforcement
learning,
SP:IC(75), 2019, pp. 158-167.
Elsevier DOI
1906
Dual-modal saliency detection, Deep reinforcement learning,
Quality-aware fusion, Generative adversarial networks
BibRef
Wang, X.F.[Xiao-Fan],
Li, S.J.[Sheng-Jie],
Image saliency prediction by learning deep probability model,
SP:IC(78), 2019, pp. 471-476.
Elsevier DOI
1909
Image saliency prediction, Probability model, Deeply-learned feature
BibRef
Fan, Y.J.[Ya Ju],
Autoencoder node saliency: Selecting relevant latent representations,
PR(88), 2019, pp. 643-653.
Elsevier DOI
1901
Autoencoder, Latent representations, Unsupervised learning,
Neural networks, Node selection, Model interpretation
BibRef
Xu, Y.Y.[Yan-Yu],
Gao, S.H.[Sheng-Hua],
Wu, J.R.[Jun-Ru],
Li, N.Y.[Nian-Yi],
Yu, J.Y.[Jing-Yi],
Personalized Saliency and Its Prediction,
PAMI(41), No. 12, December 2019, pp. 2975-2989.
IEEE DOI
1911
Observers, Saliency detection, Feature extraction, Visualization,
Semantics, Predictive models, Image color analysis,
convolutional neural network
BibRef
Zhang, Y.,
Zhang, S.,
Zhang, P.,
Song, H.,
Zhang, X.,
Local Regression Ranking for Saliency Detection,
IP(29), No. , 2020, pp. 1536-1547.
IEEE DOI
1911
Feature extraction, Saliency detection, Laplace equations,
Extraterrestrial measurements, Neural networks, Kernel,
saliency propagation
BibRef
Li, H.[Hao],
Qi, F.[Fei],
Shi, G.M.[Guang-Ming],
Lin, C.H.[Chun-Huan],
A multiscale dilated dense convolutional network for saliency
prediction with instance-level attention competition,
JVCIR(64), 2019, pp. 102611.
Elsevier DOI
1911
Saliency, Attention competition, Convolutional neural networks,
Dense connections, Dilated convolution, Multiscale features
BibRef
Ji, C.[Chao],
Huang, X.B.[Xin-Bo],
Cao, W.[Wen],
Zhu, Y.C.[Yong-Can],
Zhang, Y.[Ye],
Saliency detection using Multi-layer graph ranking and combined
neural networks,
JVCIR(65), 2019, pp. 102673.
Elsevier DOI
1912
Machine vision, Saliency detection, Fast R-CNN, Region Net, Local-Global Net
BibRef
Cai, Y.F.[Ying-Feng],
Dai, L.[Lei],
Wang, H.[Hai],
Chen, L.[Long],
Li, Y.C.[Yi-Cheng],
A Novel Saliency Detection Algorithm Based on Adversarial Learning
Model,
IP(29), 2020, pp. 4489-4504.
IEEE DOI
2003
Adversarial learning model,
discriminative models generative model, saliency detection
BibRef
Jia, S.[Sen],
Bruce, N.D.B.[Neil D.B.],
EML-NET: An Expandable Multi-Layer NETwork for saliency prediction,
IVC(95), 2020, pp. 103887.
Elsevier DOI
2004
Saliency detection, Scalability, Loss function
BibRef
Wu, Y.[Yong],
Liu, Z.[Zhi],
Zhou, X.F.[Xiao-Fei],
Saliency detection using adversarial learning networks,
JVCIR(67), 2020, pp. 102761.
Elsevier DOI
2004
Adversarial learning, Generator, discriminator,
Saliency detection, Feedback information
BibRef
Zolna, K.[Konrad],
Geras, K.J.[Krzysztof J.],
Cho, K.[Kyunghyun],
Classifier-agnostic saliency map extraction,
CVIU(196), 2020, pp. 102969.
Elsevier DOI
2006
Saliency map, Convolutional neural networks,
Image classification, Weakly supervised localization
BibRef
Radvanyi, M.[Mihaly],
Karacs, K.[Kristof],
Peeling off image layers on topographic architectures,
PRL(135), 2020, pp. 50-56.
Elsevier DOI
2006
Cellular neural network, Image hierarchy,
Saliency, Topographic processor
BibRef
Yang, S.,
Lin, G.,
Jiang, Q.,
Lin, W.,
A Dilated Inception Network for Visual Saliency Prediction,
MultMed(22), No. 8, August 2020, pp. 2163-2176.
IEEE DOI
2007
Visualization, Computational modeling, Predictive models,
Feature extraction, Spatial resolution,
inception module
BibRef
Huang, L.,
Song, K.,
Gong, A.,
Liu, C.,
Yan, Y.,
RGB-T Saliency Detection via Low-Rank Tensor Learning and Unified
Collaborative Ranking,
SPLetters(27), 2020, pp. 1585-1589.
IEEE DOI
2009
Saliency detection, Tensile stress, Feature extraction,
Signal processing algorithms, Collaboration, collaborative ranking
BibRef
Qiu, W.L.[Wen-Liang],
Gao, X.B.[Xin-Bo],
Han, B.[Bing],
Saliency detection using a deep conditional random field network,
PR(103), 2020, pp. 107266.
Elsevier DOI
2005
Saliency detection, Conditional random field, Convolutional neural network
BibRef
Liang, Y.[Ye],
Liu, H.Z.[Hong-Zhe],
Ma, N.[Nan],
A novel deep network and aggregation model for saliency detection,
VC(36), No. 9, September 2020, pp. 1883-1895.
WWW Link.
2008
BibRef
Zhou, F.,
Yao, R.,
Liao, G.,
Liu, B.,
Qiu, G.,
Visual Saliency via Embedding Hierarchical Knowledge in a Deep Neural
Network,
IP(29), 2020, pp. 8490-8505.
IEEE DOI
2008
Feature fusion, hierarchical knowledge embedding, saliency map,
task-independent saliency
BibRef
Liu, N.[Nian],
Han, J.W.[Jun-Wei],
Yang, M.H.[Ming-Hsuan],
PiCANet: Pixel-Wise Contextual Attention Learning for Accurate
Saliency Detection,
IP(29), 2020, pp. 6438-6451.
IEEE DOI
2007
BibRef
Earlier:
PiCANet: Learning Pixel-Wise Contextual Attention for Saliency
Detection,
CVPR18(3089-3098)
IEEE DOI
1812
Feature extraction, Saliency detection, Object detection,
Context modeling, Convolution, Semantics, Computational modeling,
object detection.
Feature extraction, Task analysis, Visualization, Dogs
BibRef
Pang, Y.[Yu],
Yu, X.S.[Xiao-Sheng],
Wu, Y.H.[Yun-He],
Wu, C.D.[Cheng-Dong],
Jiang, Y.[Yang],
Bagging-based saliency distribution learning for visual saliency
detection,
SP:IC(87), 2020, pp. 115928.
Elsevier DOI
2007
Saliency detection, Prior knowledge, Bagging method,
Saliency distribution learning, Saliency optimization, Prejudgment mechanism
BibRef
Borji, A.[Ali],
Saliency Prediction in the Deep Learning Era:
Successes and Limitations,
PAMI(43), No. 2, February 2021, pp. 679-700.
IEEE DOI
2101
Predictive models, Computational modeling, Benchmark testing,
Data models, Visualization, Deep learning, Task analysis, deep learning
BibRef
Ji, W.[Wei],
Yan, G.[Ge],
Li, J.J.[Jing-Jing],
Piao, Y.R.[Yong-Ri],
Yao, S.Y.[Shun-Yu],
Zhang, M.[Miao],
Cheng, L.[Li],
Lu, H.C.[Hu-Chuan],
DMRA: Depth-Induced Multi-Scale Recurrent Attention Network for RGB-D
Saliency Detection,
IP(31), 2022, pp. 2321-2336.
IEEE DOI
2203
BibRef
Earlier: A4, A1, A3, A6, A8, Only:
Depth-Induced Multi-Scale Recurrent Attention Network for Saliency
Detection,
ICCV19(7253-7262)
IEEE DOI
2004
Feature extraction, Saliency detection, Semantics,
Random access memory, Cameras, Analytical models, Visualization,
cross-modal fusion.
image colour analysis, image fusion, learning (artificial intelligence),
BibRef
Wang, Y.[Yue],
Li, Y.[Yuke],
Elder, J.H.[James H.],
Wu, R.M.[Run-Min],
Lu, H.C.[Hu-Chuan],
Zhang, L.[Lu],
Synergistic Saliency and Depth Prediction for RGB-D Saliency Detection,
ACCV20(II:336-352).
Springer DOI
2103
BibRef
Luo, X.H.[Xin-Hui],
Liu, Z.[Zhi],
Wei, W.J.[Wei-Jie],
Ye, L.W.[Lin-Wei],
Zhang, T.H.[Tian-Hong],
Xu, L.H.[Li-Hua],
Wang, J.[Jijun],
Few-shot personalized saliency prediction using meta-learning,
IVC(124), 2022, pp. 104491.
Elsevier DOI
2208
Personalized saliency prediction, Few-shot learning,
Meta-learning, Deep learning, Hard samples
BibRef
Ye, P.,
Wang, Y.,
Xia, Y.,
Enhanced Saliency Prediction via Orientation Selectivity,
VCIP20(91-95)
IEEE DOI
2102
Visualization, Feature extraction, Sensitivity, Predictive models,
Image edge detection, Computational modeling, Visual systems,
Visual Error Sensitivity
BibRef
Wang, L.,
Shen, L.,
A ConvLSTM-Combined Hierarchical Attention Network For Saliency
Detection,
ICIP20(1996-2000)
IEEE DOI
2011
Feature extraction, Saliency detection, Neural networks,
Object detection, Image edge detection, Convolution, Visualization,
Saliency Detection
BibRef
Luo, Y.[Yan],
Wong, Y.K.[Yong-Kang],
Kankanhalli, M.S.[Mohan S.],
Zhao, Q.[Qi],
n-reference Transfer Learning for Saliency Prediction,
ECCV20(VIII:502-519).
Springer DOI
2011
BibRef
Ramanathan, V.,
Dwivedi, P.,
Katabathuni, B.,
Chakraborty, A.,
Thakur, C.S.,
QUICKSAL: A small and sparse visual saliency model for efficient
inference in resource constrained hardware,
WACV20(1667-1677)
IEEE DOI
2006
Visualization, Computational modeling, Feature extraction,
Standards, Neural networks, Kernel, Hardware
BibRef
Zhao, T.[Ting],
Wu, X.Q.[Xiang-Qian],
Pyramid Feature Attention Network for Saliency Detection,
CVPR19(3080-3089).
IEEE DOI
2002
BibRef
Zhang, K.[Kaihua],
Li, T.[Tengpeng],
Liu, B.[Bo],
Liu, Q.S.[Qing-Shan],
Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With
Multi-Scale Label Smoothing,
CVPR19(3090-3099).
IEEE DOI
2002
BibRef
He, S.[Sen],
Tavakoli, H.R.[Hamed R.],
Borji, A.[Ali],
Mi, Y.[Yang],
Pugeault, N.[Nicolas],
Understanding and Visualizing Deep Visual Saliency Models,
CVPR19(10198-10207).
IEEE DOI
2002
BibRef
Zhu, C.,
Yan, W.,
Liu, S.,
Li, T.,
Li, G.,
Salient Contour-Aware Based Twice Learning Strategy for Saliency
Detection,
ACVR19(2541-2548)
IEEE DOI
2004
convolutional neural nets,
learning (artificial intelligence), object detection,
twice learning strategy
BibRef
Zhang, J.,
Zhang, T.,
Daf, Y.,
Harandi, M.,
Hartley, R.,
Deep Unsupervised Saliency Detection:
A Multiple Noisy Labeling Perspective,
CVPR18(9029-9038)
IEEE DOI
1812
Noise measurement, Saliency detection, Predictive models, Training,
Neural networks, Optimization, Labeling
BibRef
Kong, P.[Phutphalla],
Mancas, M.[Matei],
Thuon, N.[Nimol],
Kheang, S.[Seng],
Gosselin, B.[Bernard],
Do Deep-Learning Saliency Models Really Model Saliency?,
ICIP18(2331-2335)
IEEE DOI
1809
Detectors, Face, Measurement, Visualization, Animals, Transportation,
Computational modeling, attention, saliency, DNN, bottom-up, top-down,
text detection
BibRef
Wang, T.,
Zhang, L.,
Wang, S.,
Lu, H.,
Yang, G.,
Ruan, X.,
Borji, A.,
Detect Globally, Refine Locally: A Novel Approach to Saliency
Detection,
CVPR18(3127-3135)
IEEE DOI
1812
Convolution, Saliency detection, Visualization, Feature extraction,
Neural networks, Object detection, Semantics
BibRef
Zeng, Y.,
Lu, H.,
Zhang, L.,
Feng, M.,
Borji, A.,
Learning to Promote Saliency Detectors,
CVPR18(1644-1653)
IEEE DOI
1812
Training, Saliency detection, Feature extraction,
Artificial neural networks, Testing, Task analysis, Measurement
BibRef
Yan, B.[Bing],
Wang, H.Q.[Hao-Qian],
Wang, X.Z.[Xing-Zheng],
Zhang, Y.B.[Yong-Bing],
An accurate saliency prediction method based on generative
adversarial networks,
ICIP17(2339-2343)
IEEE DOI
1803
Cost function, Feature extraction, Games,
Task analysis, Training, Visualization, GAN, Saliency prediction,
saliency network
BibRef
Imamoglu, N.,
Zhang, C.,
Shmoda, W.,
Fang, Y.,
Shi, B.,
Saliency detection by forward and backward cues in deep-CNN,
ICIP17(430-434)
IEEE DOI
1803
convolutional neural networks,
partially-guided back-propagation, saliency detection
BibRef
Jin, G.,
Shen, S.,
Zhang, D.,
Duan, W.,
Zhang, Y.,
Deep saliency map estimation of hand-crafted features,
ICIP17(4262-4266)
IEEE DOI
1803
Benchmark testing, Estimation, Feature extraction,
Machine learning, Pipelines, Saliency detection, Visualization,
Saliency detection
BibRef
Zhu, L.,
Ling, H.,
Wu, J.,
Deng, H.,
Liu, J.,
Saliency Pattern Detection by Ranking Structured Trees,
ICCV17(5468-5477)
IEEE DOI
1802
feature extraction, image representation,
learning (artificial intelligence), object detection,
Proposals
BibRef
Cornia, M.,
Baraldi, L.,
Serra, G.,
Cucchiara, R.,
A deep multi-level network for saliency prediction,
ICPR16(3488-3493)
IEEE DOI
1705
Benchmark testing, Convolutional codes,
Encoding, Feature extraction, Measurement, Observers
BibRef
Pan, J.,
Sayrol, E.,
Giró-i-Nieto, X.[Xavier],
McGuinness, K.[Kevin],
O'Connor, N.E.,
Shallow and Deep Convolutional Networks for Saliency Prediction,
CVPR16(598-606)
IEEE DOI
1612
BibRef
Salvador, A.,
Giró-i-Nieto, X.[Xavier],
Marqués, F.[Ferran],
Faster R-CNN Features for Instance Search,
DeepLearn-C16(394-401)
IEEE DOI
1612
BibRef
Cornia, M.[Marcella],
Baraldi, L.[Lorenzo],
Serra, G.[Giuseppe],
Cucchiara, R.[Rita],
Multi-level Net: A Visual Saliency Prediction Model,
ACVR16(II: 302-315).
Springer DOI
1611
BibRef
Marighetto, P.,
Abdelkader, I.H.,
Duzelier, S.,
Décombas, M.,
Riche, N.,
Jakubowicz, J.,
Mancas, M.,
Gosselin, B.,
Laganière, R.,
FUNNRAR: Hybrid rarity/learning visual saliency,
ICIP16(2782-2786)
IEEE DOI
1610
Artificial neural networks
BibRef
Huang, X.,
Shen, C.,
Boix, X.,
Zhao, Q.,
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting
Deep Neural Networks,
ICCV15(262-270)
IEEE DOI
1602
Computational modeling
BibRef
Zhang, D.W.[Ding-Wen],
Han, J.W.[Jun-Wei],
Li, C.[Chao],
Wang, J.D.[Jing-Dong],
Co-saliency detection via looking deep and wide,
CVPR15(2994-3002)
IEEE DOI
1510
BibRef
Wang, L.J.[Li-Jun],
Lu, H.C.[Hu-Chuan],
Ruan, X.[Xiang],
Yang, M.H.[Ming-Hsuan],
Deep networks for saliency detection via local estimation and global
search,
CVPR15(3183-3192)
IEEE DOI
1510
BibRef
Vig, E.[Eleonora],
Dorr, M.[Michael],
Cox, D.[David],
Large-Scale Optimization of Hierarchical Features for Saliency
Prediction in Natural Images,
CVPR14(2798-2805)
IEEE DOI
1409
deep learning, hyperparameter optimization, saliency
BibRef
Li, G.B.[Guan-Bin],
Yu, Y.Z.[Yi-Zhou],
Visual Saliency Detection Based on Multiscale Deep CNN Features,
IP(25), No. 11, November 2016, pp. 5012-5024.
IEEE DOI
1610
BibRef
And:
Deep Contrast Learning for Salient Object Detection,
CVPR16(478-487)
IEEE DOI
1612
BibRef
Earlier:
Visual saliency based on multiscale deep features,
CVPR15(5455-5463)
IEEE DOI
1510
feature extraction
BibRef
Liu, R.S.[Ri-Sheng],
Cao, J.J.[Jun-Jie],
Lin, Z.C.[Zhou-Chen],
Shan, S.G.[Shi-Guang],
Adaptive Partial Differential Equation Learning for Visual Saliency
Detection,
CVPR14(3866-3873)
IEEE DOI
1409
Learning-Based PDEs, Saliency Detection, Submodular Optimization
BibRef
Hong, Y.[Yi],
Jiang, J.Y.[Jia-Yan],
Tu, Z.W.[Zhuo-Wen],
Sparse semi-supervised learning for perceptual grouping,
POCV10(1-8).
IEEE DOI
1006
BibRef
Orabona, F.[Francesco],
Metta, G.[Giorgio],
Sandini, G.[Giulio],
Learning Association Fields from Natural Images,
PercOrg06(174).
IEEE DOI
0609
BibRef
Driancourt, R.[Remi],
Learning Perceptual Organization with a Developmental Robot,
PercOrg04(60).
IEEE DOI
0502
BibRef
Derou, D.[Dominique],
Herault, L.[Laurent],
Pulsed neural networks and perceptive grouping,
ECCV94(A:521-526).
Springer DOI
9405
BibRef
Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Grouping, Lines and Curves .