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Thanks to the rapid advances in deep learning techniques and the wide availability of large-scale training sets, the performance of video saliency detection models has been improving steadily and significantly.
J. Podlesny and D. Raskin, “Physiological measures and the detection of deception.” Psychological bulletin , vol. 84, no. 4, p. 782, 1977
1977
Earlier work this paper cites.
L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” TPAMI , vol. 20, no. 11, pp. 1254–1259, 1998
1998
Earlier work this paper cites.
ShewchenkoN, WithnallC, KeownM, GittensR, and DvorakJ, “Heading in football. part 1: development of biomechanical methods to investigate head response,” BJSM , vol. 39, no. 1, pp. 10–25, 2005
2005
Earlier work this paper cites.
J. Harel, C. Koch, and P. Perona, “Graph-based visual saliency,” in NeurIPS , 2007
2007
Earlier work this paper cites.
L. Bottou and O. Bousquet, “The tradeoffs of large scale learning,” in NeurIPS , 2008
2008
Earlier work this paper cites.
M. Rodriguez, J. Ahmed, and M. Shah, “Action mach a spatio-temporal maximum average correlation height filter for action recognition,” in CVPR , 2008
2008
Earlier work this paper cites.
M. Marszalek, I. Laptev, and C. Schmid, “Actions in context,” in CVPR , 2009
2009
Earlier work this paper cites.
P. Mital, T. Smith, R. Hill, and J. Henderson, “Clustering of gaze during dynamic scene viewing is predicted by motion,” COGN COMPUT , vol. 3, no. 1, pp. 5–24, 2011
2011
Earlier work this paper cites.
A. Borji and L. Itti, “State-of-the-art in visual attention modeling,” TPAMI , vol. 35, no. 1, pp. 185–207, 2012
2012
Earlier work this paper cites.
M. Shigeoka, N. Urakawa, T. Nakamura, M. Nishio, T. Watajima, D. Kuroda, T. Komori, Y. Kakeji, S. Semba, and H. Yokozaki, “Tumor associated macrophage expressing cd 204 is associated with tumor aggressiveness of esophageal squamous cell carcinoma,” Cancer science , vol. 104, no. 8, pp. 1112–1119, 2013
2013
Earlier work this paper cites.
D. Rudoy, D. Goldman, E. Shechtman, and L. ZelnikManor, “Learning video saliency from human gaze using candidate selection,” in CVPR , 2013
2013
Earlier work this paper cites.
A. Coutrot and N. Guyader, “How saliency, faces, and sound influence gaze in dynamic social scenes,” JoV , vol. 14, no. 8, pp. 5–5, 2014
2014
Earlier work this paper cites.
M. Gygli, H. Grabner, H. Riemenschneider, and L. VanGool, “Creating summaries from user videos,” in ECCV , 2014
2014
Earlier work this paper cites.
M. Jiang, S. Huang, J. Duan, and Q. Zhao, “Salicon: Saliency in context,” in CVPR , 2015
2015
Earlier work this paper cites.
J. Dai, K. He, and J. Sun, “Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation,” in ICCV , 2015
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in ICLR , 2015
2015
Earlier work this paper cites.
P. Koutras and P. Maragos, “A perceptually based spatio-temporal computational framework for visual saliency estimation,” SP:IC , vol. 38, pp. 15–31, 2015
2015
Earlier work this paper cites.
J. Pan, E. Sayrol, X. GiroiNieto, K. McGuinness, and N. OConnor, “Shallow and deep convolutional networks for saliency prediction,” in CVPR , 2016
2016
Earlier work this paper cites.
B. Mandal, L. Li, G. S. Wang, and J. Lin, “Towards detection of bus driver fatigue based on robust visual analysis of eye state,” TITS , vol. 18, no. 3, pp. 545–557, 2016
2016
Earlier work this paper cites.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in CVPR , 2016
2016
Earlier work this paper cites.
Z. Lu, Z. Fu, T. Xiang, P. Han, L. Wang, and X. Gao, “Learning from weak and noisy labels for semantic segmentation,” TPAMI , vol. 39, no. 3, pp. 486–500, 2016
2016
Earlier work this paper cites.
X. Min, G. Zhai, K. Gu, and X. Yang, “Fixation prediction through multimodal analysis,” ACM TOMM , 2016
2016
Earlier work this paper cites.
A. Coutrot and N. Guyader, “Multimodal saliency models for videos,” in From Human Attention to Computational Attention , 2016, pp. 291–304
2016
Earlier work this paper cites.
V. Leboran, A. GarciaDiaz, X. FdezVidal, and X. Pardo, “Dynamic whitening saliency,” TPAMI , vol. 39, no. 5, pp. 893–907, 2016
2016
Earlier work this paper cites.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in CVPR , 2016
2016
Earlier work this paper cites.
C. Chen, S. Li, Y. Wang, A. Hao, and H. Qin, “Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion,” TIP , vol. 26, no. 7, pp. 3156–3170, 2017
2017
Earlier work this paper cites.
C. Bak, A. Kocak, E. Erdem, and A. Erdem, “Spatio-temporal saliency networks for dynamic saliency prediction,” TMM , vol. 20, no. 7, pp. 1688–1698, 2017
2017
Earlier work this paper cites.
W. Wang and J. Shen, “Deep visual attention prediction,” TIP , vol. 27, no. 5, pp. 2368–2378, 2017
2017
Earlier work this paper cites.
E. Kok and H. Jarodzka, “Before your very eyes: The value and limitations of eye tracking in medical education,” Medical education , vol. 51, no. 1, pp. 114–122, 2017
2017
Earlier work this paper cites.
L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with image-level supervision,” in CVPR , 2017
2017
Earlier work this paper cites.
Z. Shi, Y. Yang, T. M. Hospedales, and T. Xiang, “Weakly-supervised image annotation and segmentation with objects and attributes,” TPAMI , vol. 39, no. 12, pp. 2525–2538, 2017
2017
Earlier work this paper cites.
T. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017
2017
Earlier work this paper cites.
J. Pan, C. Ferrer, K. McGuinness, N. OConnor, J. Torres, E. Sayrol, and X. GiroiNieto, “Salgan: Visual saliency prediction with generative adversarial networks,” CVPR SUNw , 2017
2017
Earlier work this paper cites.
J. Gemmeke, D. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. Moore, M. Plakal, and M. Ritter, “Audio set: An ontology and human-labeled dataset for audio events,” in ICASSP , 2017
2017
Earlier work this paper cites.
D. Zhang, J. Han, and Y. Zhang, “Supervision by fusion: Towards unsupervised learning of deep salient object detector,” in ICCV , 2017
2017
Earlier work this paper cites.
R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in ICCV , 2017
2017
Earlier work this paper cites.
Y. Fang, G. Ding, J. Li, and Z. Fang, “Deep3dsaliency: Deep stereoscopic video saliency detection model by 3d convolutional networks,” TIP , vol. 28, no. 5, pp. 2305–2318, 2018
2018
Cited alongside, same era.
Y. Xu, S. Gao, J. Wu, N. Li, and J. Yu, “Personalized saliency and its prediction,” TPAMI , vol. 41, no. 12, pp. 2975–2989, 2018
2018
Cited alongside, same era.
M. Sun, Z. Zhou, Q. Hu, Z. Wang, and J. Jiang, “Sg-fcn: A motion and memory-based deep learning model for video saliency detection,” TCYB , vol. 49, no. 8, pp. 2900–2911, 2018
2018
Cited alongside, same era.
S. Gorji and J. Clark, “Going from image to video saliency: Augmenting image salience with dynamic attentional push,” in CVPR , 2018
2018
Cited alongside, same era.
Y. Zhu, G. Zhai, and X. Min, “The prediction of head and eye movement for 360 degree images,” SP:IC , vol. 69, pp. 15–25, 2018
Z. Yang, D. Mahajan, D. Ghadiyaram, R. Nevatia, and V. Ramanathan, “Activity driven weakly supervised object detection,” in CVPR , 2019
2019
Later among the works it cites.
W. Wang, X. Lu, J. Shen, D. J. Crandall, and L. Shao, “Zero-shot video object segmentation via attentive graph neural networks,” in ICCV , 2019
2019
Later among the works it cites.
T. Yang and A. Chan, “Visual tracking via dynamic memory networks,” TPAMI , vol. 43, no. 1, pp. 360–374, 2019
2019
Later among the works it cites.
W. Sun, Z. Chen, and F. Wu, “Visual scanpath prediction using ior-roi recurrent mixture density network,” TPAMI , vol. 43, no. 6, pp. 2101–2118, 2019
2019
Later among the works it cites.
A. Borji, “Saliency prediction in the deep learning era: Successes and limitations,” TPAMI , 2019
2019
Later among the works it cites.
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2018
Cited alongside, same era.
Y. Tian, J. Shi, B. Li, Z. Duan, and C. Xu, “Audio-visual event localization in unconstrained videos,” in ECCV , 2018
2018
Cited alongside, same era.
S. Bargal, A. Zunino, D. Kim, J. Zhang, V. Murino, and S. Sclaroff, “Excitation backprop for rnns,” in CVPR , 2018
2018
Cited alongside, same era.
A. Chattopadhay, A. Sarkar, P. Howlader, and V. Balasubramanian, “Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks,” in WACV , 2018
2018
Cited alongside, same era.
F. S. Saleh, M. S. Aliakbarian, M. Salzmann, L. Petersson, J. Alvarez, and S. Gould, “Incorporating network built-in priors in weakly-supervised semantic segmentation,” TPAMI , vol. 40, no. 6, pp. 1382–1396, 2018
2018
Cited alongside, same era.
X. Zhang, Y. Wei, J. Feng, Y. Yang, and T. Huang, “Adversarial complementary learning for weakly supervised object localization,” in CVPR , 2018
2018
Cited alongside, same era.
N. Inoue, R. Furuta, T. Yamasaki, and K. Aizawa, “Cross-domain weakly-supervised object detection through progressive domain adaptation,” in CVPR , 2018
2018
Cited alongside, same era.
Y. Lu, J. Yin, Z. Chen, H. Gong, Y. Liu, L. Qian, X. Li, R. Liu, I. Andolina, and W. Wang, “Revealing detail along the visual hierarchy: neural clustering preserves acuity from v1 to v4,” Neuron , vol. 98, no. 2, pp. 417–428, 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
X. Wu, Z. Wu, J. Zhang, L. Ju, and S. Wang, “Salsac: A video saliency prediction model with shuffled attentions and correlation-based convlstm,” in AAAI , 2020
2020
Later among the works it cites.
Y. Gu, L. Wang, Z. Wang, Y. Liu, M.-M. Cheng, and S. Lu, “Pyramid constrained self-attention network for fast video salient object detection,” in AAAI , 2020
2020
Later among the works it cites.
B. Wang, W. Liu, G. Han, and S. He, “Learning long-term structural dependencies for video salient object detection,” TIP , vol. 29, pp. 9017–9031, 2020
2020
Later among the works it cites.
S. Ren, C. Han, X. Yang, G. Han, and S. He, “Tenet: Triple excitation network for video salient object detection,” in ECCV , 2020
2020
Later among the works it cites.
C. Chen, G. Wang, C. Peng, X. Zhang, and H. Qin, “Improved robust video saliency detection based on long-term spatial-temporal information,” TIP , vol. 29, no. 1, pp. 1090–1100, 2020
2020
Later among the works it cites.
X. Wu, Z. Wu, J. Zhang, L. Ju, and S. Wang, “Salsac: A video saliency prediction model with shuffled attentions and correlation-based convlstm,” in AAAI , 2020
2020
Later among the works it cites.
R. Droste, J. Jiao, and J. Noble, “Unified image and video saliency modeling,” ECCV , 2020
2020
Later among the works it cites.
A. Tsiami, P. Koutras, and P. Maragos, “Stavis: Spatio-temporal audiovisual saliency network,” in CVPR , 2020
2020
Later among the works it cites.
J. Zhang, X. Yu, A. Li, P. Song, B. Liu, and Y. Dai, “Weakly-supervised salient object detection via scribble annotations,” in CVPR , 2020
2020
Later among the works it cites.
B. Zhang, J. Xiao, Y. Wei, M. Sun, and K. Huang, “Reliability does matter: An end-to-end weakly supervised semantic segmentation approach,” in AAAI , 2020
2020
Later among the works it cites.
Z. Chen, Z. Fu, R. Jiang, Y. Chen, and X. Hua, “Slv: Spatial likelihood voting for weakly supervised object detection,” in CVPR , 2020
2020
Later among the works it cites.
H. Chen, W. Xie, A. Vedaldi, and A. Zisserman, “Vggsound: A large-scale audio-visual dataset,” in ICASSP , 2020
2020
Later among the works it cites.
G. Sun, W. Wang, J. Dai, and L. VanGool, “Mining cross-image semantics for weakly supervised semantic segmentation,” in ECCV , 2020
2020
Later among the works it cites.
B. Zhang, D. Xiong, J. Xie, and J. Su, “Neural machine translation with gru-gated attention model,” TNNLS , vol. 31, no. 11, pp. 4688–4698, 2020
2020
Later among the works it cites.
R. Fu, Q. Hu, X. Dong, Y. Guo, Y. Gao, and B. Li, “Axiom-based grad-cam: Towards accurate visualization and explanation of cnns,” BMVC , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Wang, Z. Wang, M. Du, F. Yang, Z. Zhang, S. Ding, P. Mardziel, and X. Hu, “Score-cam: Score-weighted visual explanations for convolutional neural networks,” in CVPRW , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Ramaswamy et al. , “Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization,” in WACV , 2020
2020
Later among the works it cites.
M. Muhammad and M. Yeasin, “Eigen-cam: Class activation map using principal components,” in IJCNN , 2020
2020
Later among the works it cites.
G. Wang, C. Chen, D.-P. Fan, A. Hao, and H. Qin, “From semantic categories to fixations: A novel weakly-supervised visual-auditory saliency detection approach,” in CVPR , 2021
2021
Closest in time.
W. Wang, J. Shen, J. Xie, M.-M. Cheng, H. Ling, and A. Borji, “Revisiting video saliency prediction in the deep learning era,” TPAMI , vol. 43, no. 1, pp. 220–237, 2021
2021
Closest in time.
Y. Zhu, G. Zhai, Y. Yang, H. Duan, X. Min, and X. Yang, “Viewing behavior supported visual saliency predictor for 360 degree videos,” TCSVT , 2021
2021
Closest in time.
Z. Li, W. Wang, Z. Li, Y. Huang, and Y. Sato, “Towards visually explaining video understanding networks with perturbation,” in WACV , 2021
2021
Closest in time.
P. Jiang, L. Han, Q. Hou, M.-M. Cheng, and Y. Wei, “Online attention accumulation for weakly supervised semantic segmentation,” TPAMI , 2021
2021
Closest in time.
J. Choe, S. Lee, and H. Shim, “Attention-based dropout layer for weakly supervised single object localization and semantic segmentation,” TPAMI , vol. 43, no. 12, pp. 4256–4271, 2021
2021
Closest in time.
X. Li, T. Zhou, J. Li, Y. Zhou, and Z. Zhang, “Group-wise semantic mining for weakly supervised semantic segmentation,” AAAI , 2021
2021
Closest in time.
C. Xia, J. Han, and D. Zhang, “Evaluation of saccadic scanpath prediction: Subjective assessment database and recurrent neural network based metric,” TPAMI , vol. 43, no. 12, pp. 4378–4395, 2021
2021
Closest in time.
P. Jiang, C. Zhang, Q. Hou, M.-M. Cheng, and Y. Wei, “Layercam: Exploring hierarchical class activation maps for localization,” TIP , vol. 30, pp. 5875–5888, 2021
2021
Closest in time.