15.1.2 Active Vision, Visual Attention

James, W.,
The Principles of Psychology,
HoltNew York, 1890. Initial discussion of attention mechanisms. BibRef 9000

Srinivasan, M.V., Thathachar, M.A.L., Deekshatulu, B.L.,
A Probabilistic Hypothesis for the Prediction of Visual Fixations,
SMC(5), 1975, pp. 431-437. BibRef 7500

Leavers, V.F.,
Preattentive Computer Vision: Towards a 2-Stage Computer Vision System for the Extraction of Qualitative Descriptors and the Cues for Focus of Attention,
IVC(12), No. 9, November 1994, pp. 583-599.
Elsevier DOI BibRef 9411

Pretlove, J.R.G., Parker, G.A.,
The Surrey Attentive Robot Vision System,
PRAI(7), No. 1, February 1993, 1993, pp. 89-107. BibRef 9302
Earlier:
Lightweight Camera Head for Robotic-Based Binocular Stereo Vision: An Integrated Engineering Approach,
SPIE(1708), 1992, pp. 62-75. BibRef

Sakane, S., Kuruma, T., Omata, T., Sato, T.,
Planning Focus of Attention for Multifingered Hand with Consideration of Time-Varying Aspects,
CVIU(61), No. 3, May 1995, pp. 445-453.
DOI Link BibRef 9505

Tsotsos, J.K.[John K.], Culhane, S.M.[Scan M.], Wai, W.Y.K.[Winky Yan Kei], Lai, Y.Z.[Yu-Zhong], Davis, N.[Neal], Nuflo, F.[Fernando],
Modeling Visual-Attention Via Selective Tuning,
AI(78), No. 1-2, October 1995, pp. 507-545.
Elsevier DOI BibRef 9510

Culhane, S.M.[Scan M.], Tsotsos, J.K.[John K.],
An Attentional Prototype for Early Vision,
ECCV92(551-560).
Springer DOI BibRef 9200
Earlier:
A Prototype for Data-Driven Visual Attention,
ICPR92(I:36-40).
IEEE DOI BibRef

Concepcion, V.[Vicente], Wechsler, H.[Harry],
Detection and Localization of Objects in Time-varying Imagery Using Attention, Representation and Memory Pyramids,
PR(29), No. 9, September 1996, pp. 1543-1557.
Elsevier DOI BibRef 9609
Earlier:
Multiresolution attention and associative memory systems for time-varying imagery,
ICPR94(A:840-842).
IEEE DOI 9410
BibRef

Pau, L.F.,
An Intelligent Camera for Active Vision,
PRAI(10), No. 1, 1996, pp. 33-42. BibRef 9600

Sela, G., Levine, M.D.,
Real-Time Attention for Robotic Vision,
RealTimeImg(3), No. 3, June 1997, pp. 173-194. 9708
BibRef

Baluja, S.[Shumeet], Pomerleau, D.A.[Dean A.],
Dynamic Relevance: Vision-Based Focus of Attention Using Artificial Neural Networks,
AI(97), No. 1-2, December 1997, pp. 381-395.
Elsevier DOI 9801
BibRef

Itti, L.[Laurent], Koch, C.[Cristof], Niebur, E.[Ernst],
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis,
PAMI(20), No. 11, November 1998, pp. 1254-1259.
IEEE DOI 9811
Attention mechanism based on neuronal structures. BibRef

Itti, L.[Laurent], Koch, C.[Christof],
Saliency-Based Search Mechanism for Overt and Covert Shifts of Visual Attention,
Vision Research(40), Nos. 10-12, 2000, pp. 1489-1506. BibRef 0001
Earlier:
Learning to Detect Salient Objects in Natural Scenes Using Visual Attention,
DARPA98(1201-1206). BibRef

Itti, L.[Laurent],
Automatic Foveation for Video Compression Using a Neurobiological Model of Visual Attention,
IP(13), No. 10, October 2004, pp. 1304-1318.
IEEE DOI 0410
BibRef

Alvarez, J.R.S.[J.R. Serra], Subirana-Vilanova, J.B.[J. Brian],
Texture frame curves and regions of attention using adaptive non-cartesian networks,
PR(32), No. 3, March 1999, pp. 503-515.
Elsevier DOI BibRef 9903

Stough, T.M., Brodley, C.E.,
Focusing attention on objects of interest using multiple matched filters,
IP(10), No. 3, March 2001, pp. 419-426.
IEEE DOI 0104
BibRef

Cantoni, V.[Virginio], Marinaro, M.[Maria], Petrosino, A.[Alfredo],
Visual Attention Mechanisms,
KluwerDecember 2002, ISBN 0-306-47427-1.
WWW Link. BibRef 0212

Bozma, H.I., Çakiroglu, G., Soyer, Ç.,
Biologically inspired Cartesian and non-Cartesian filters for attentional sequences,
PRL(24), No. 9-10, June 2003, pp. 1261-1274.
Elsevier DOI 0304
BibRef

Ouerhani, N.[Nabil], Hügli, H.[Heinz],
Real-Time visual attention on a massively parallel SIMD architecture,
RealTimeImg(9), No. 3, June 2003, pp. 189-196.
Elsevier DOI 0310
BibRef

Ouerhani, N., Bracamonte, J., Hugli, H., Ansorge, M., Pellandini, F.,
Adaptive color image compression based on visual attention,
CIAP01(416-421).
IEEE DOI 0210
BibRef

Ouerhani, N., Hügli, H., Burgi, P.Y., Ruedi, P.F.,
A Real Time Implementation of the Saliency-Based Model of Visual Attention on a SIMD Architecture,
DAGM02(282 ff.).
Springer DOI 0303
BibRef

Ouerhani, N.[Nabil], von Wartburg, R.[Roman], Hugli, H.[Heinz],
Empirical Validation of the Saliency-based Model of Visual Attention,
ELCVIA(3), No. 1, 2004, pp. 13-24.
DOI Link 0402
BibRef

Ouerhani, N.[Nabil], Bur, A.[Alexandre], Hügli, H.[Heinz],
Linear vs. Nonlinear Feature Combination for Saliency Computation: A Comparison with Human Vision,
DAGM06(314-323).
Springer DOI 0610
BibRef

Frintrop, S.[Simone], Rome, E.[Erich], Nuchter, A.[Andreas], Surmann, H.[Hartmut],
A Bimodal Laser-Based Attention System,
CVIU(100), No. 1-2, October-November 2005, pp. 124-151.
Elsevier DOI 0510
BibRef

Frintrop, S.[Simone], Backer, G.[Gerriet], Rome, E.[Erich],
Goal-Directed Search with a Top-Down Modulated Computational Attention System,
DAGM05(117).
Springer DOI 0509
BibRef

Frintrop, S.[Simone], Rome, E.[Erich], Nüchter, A.[Andreas], Surmann, H.[Hartmut],
An Attentive, Multi-modal Laser 'Eye',
CVS03(202 ff).
Springer DOI 0306
BibRef

Frintrop, S.[Simone],
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search,
Springer2006, BibRef 0600 LNCS3899. ISBN: 3-540-32759-2.
Springer DOI From the thesis. BibRef

Draper, B.A.[Bruce A.], Lionelle, A.[Albert],
Evaluation of selective attention under similarity transformations,
CVIU(100), No. 1-2, October-November 2005, pp. 152-171.
Elsevier DOI 0510
BibRef

Williams, T.J., Draper, B.A.,
An Evaluation of Motion in Artificial Selective Attention,
AttenPerf05(III: 85-85).
IEEE DOI 0507
BibRef

Heinke, D.[Dietmar], Humphreys, G.W.[Glyn W.],
Selective Attention for Identification Model: Simulating visual neglect,
CVIU(100), No. 1-2, October-November 2005, pp. 172-197.
Elsevier DOI 0510
BibRef

Backhaus, A., Heinke, D.[Dietmar], Humphreys, G.W.[Glyn W.],
Contextual Learning in the Selective Attention for Identification model (CL-SAIM): Modeling contextual cueing in visual search tasks,
AttenPerf05(III: 87-87).
IEEE DOI 0507
BibRef

Jost, T.[Timothee], Ouerhani, N.[Nabil], von Wartburg, R.[Roman], Muri, R.[Rene], Hugli, H.[Heinz],
Assessing the contribution of color in visual attention,
CVIU(100), No. 1-2, October-November 2005, pp. 107-123.
Elsevier DOI 0510
BibRef

Hamker, F.H.[Fred H.],
The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision,
CVIU(100), No. 1-2, October-November 2005, pp. 64-106.
Elsevier DOI 0510
BibRef

Walther, D.B.[Dirk B.], Rutishauser, U.[Ueli], Koch, C.[Christof], Perona, P.[Pietro],
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes,
CVIU(100), No. 1-2, October-November 2005, pp. 41-63.
Elsevier DOI 0510
BibRef

Avraham, T.[Tamar], Lindenbaum, M.[Michael],
Attention-Based Dynamic Visual Search Using Inner-Scene Similarity: Algorithms and Bounds,
PAMI(28), No. 2, February 2006, pp. 251-264.
IEEE DOI 0601
BibRef
Earlier:
Dynamic Visual Search Using Inner-Scene Similarity: Algorithms and Inherent Limitations,
ECCV04(Vol II: 58-70).
Springer DOI 0405
Visually similar are more likely the same thing. BibRef

Avraham, T.[Tamar], Lindenbaum, M.[Michael],
Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling,
PAMI(32), No. 4, April 2010, pp. 693-708.
IEEE DOI 1003
Allocate resources based on importance. Bottom-up attention model, probability that a part of the image is of interest. BibRef

Horaud, R.[Radu], Knossow, D.[David], Michaelis, M.[Markus],
Camera cooperation for achieving visual attention,
MVA(16), No. 6, 2006, pp. 330-342.
Springer DOI 0603
BibRef

López, M.T.[María T.], Fernández-Caballero, A.[Antonio], Fernández, M.A.[Miguel A.], Mira, J.[José], Delgado, A.E.[Ana E.],
Motion features to enhance scene segmentation in active visual attention,
PRL(27), No. 5, 1 April 2006, pp. 469-478.
Elsevier DOI 0604
Permanency memories; Segmentation; Feature extraction BibRef

Lopez, M.T.[Maria T.], Fernandez, M.A.[Miguel A.], Fernandez-Caballero, A.[Antonio], Mira, J.[Jose], Delgado, A.E.[Ana E.],
Dynamic visual attention model in image sequences,
IVC(25), No. 5, 1 May 2007, pp. 597-613.
Elsevier DOI 0703
Dynamic visual attention; Motion; Segmentation; Feature extraction; Feature integration BibRef

Machrouh, J.[Joseph], Tarroux, P.[Philippe],
Attentional Mechanisms for Interactive Image Exploration,
JASP(2005), No. 14, 2005, pp. 2391-2396.
WWW Link. 0603
BibRef

Le Meur, O.[Olivier], Le Callet, P.[Patrick], Barba, D.[Dominique], and Thoreau, D.[Dominique],
A Coherent Computational Approach to Model Bottom-Up Visual Attention,
PAMI(28), No. 5, May 2006, pp. 802-817.
IEEE DOI 0604
BibRef
Earlier:
Performance assessment of a visual attention system entirely based on a human vision modeling,
ICIP04(IV: 2327-2330).
IEEE DOI 0505
BibRef
And: A1, A4, A2, A3:
A Spatio-Temporal Model of the Selective Human Visual Attention,
ICIP05(III: 1188-1191).
IEEE DOI 0512
Uses current views on HVS models of attention. Contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions are used. Compares with:
See also Model of Saliency-Based Visual Attention for Rapid Scene Analysis, A. BibRef

Le Meur, O.[Olivier], Castellan, X., Le Callet, P.[Patrick], Barba, D.[Dominique],
Efficient Saliency-Based Repurposing Method,
ICIP06(421-424).
IEEE DOI 0610
BibRef

Stentiford, F.[Fred],
Attention-based similarity,
PR(40), No. 3, March 2007, pp. 771-783.
Elsevier DOI 0611
Visual attention; Similarity; Shape; Image retrieval; CBIR BibRef

Shic, F.[Frederick], Scassellati, B.[Brian],
A Behavioral Analysis of Computational Models of Visual Attention,
IJCV(73), No. 2, June 2007, pp. 159-177.
Springer DOI 0702
Quantative analysis of attention models. BibRef

Dong, L.[Le], Izquierdo, E.[Ebroul],
A Biologically Inspired System for Classification of Natural Images,
CirSysVideo(17), No. 5, May 2007, pp. 590-603.
IEEE DOI 0705
Visual attention model. BibRef

Rothenstein, A.L.[Albert L.], Tsotsos, J.K.[John K.],
Attention links sensing to recognition,
IVC(26), No. 1, 1 January 2008, pp. 114-126.
Elsevier DOI 0711
Cognitive vision; Attention; Recognition; Selective tuning BibRef

Hu, Y.Q.[Yi-Qun], Rajan, D.[Deepu], Chia, L.T.[Liang-Tien],
Detection of visual attention regions in images using robust subspace analysis,
JVCIR(19), No. 3, April 2008, pp. 199-216.
Elsevier DOI 0803
Visual attention; Subspace analysis; Saliency; GPCA; Scale-space; Clustering; Least square estimation BibRef

Bermudez-Contreras, E., Buxton, H., Spier, E.,
Attention can improve a simple model for object recognition,
IVC(26), No. 6, 1 June 2008, pp. 776-787.
Elsevier DOI 0804
Object recognition; HMAX; Foveation; Attention; Active vision; Visual cortex; Translation invariance; Scale invariance BibRef

Aziz, M.Z.[Muhammad Zaheer], Mertsching, B.[Bärbel],
Fast and Robust Generation of Feature Maps for Region-Based Visual Attention,
IP(17), No. 5, May 2008, pp. 633-644.
IEEE DOI 0804
BibRef
And:
Visual Search in Static and Dynamic Scenes Using Fine-Grain Top-Down Visual Attention,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Aziz, M.Z.[Muhammad Zaheer], Mertsching, B.[Bärbel],
Fast Depth Saliency from Stereo for Region-Based Artificial Visual Attention,
ACIVS10(I: 367-378).
Springer DOI 1012
BibRef

Aziz, M.Z.[M. Zaheer], Mertsching, B.[Barbel], Salah, M., Shafik, E.N., Stemmer, R.[Ralf],
Evaluation of Visual Attention Models for Robots,
CVS06(20).
IEEE DOI 0602
BibRef

Lang, C.Y.[Cong-Yan], Xu, D.[De], Li, N.[Ning],
Modeling Bottom-Up Visual Attention for Color Images,
IEICE(E91-D), No. 3, March 2008, pp. 869-872.
DOI Link 0803
BibRef

Sevilmis, T.[Tarkan], Bastan, M.[Muhammet], Güdükbay, U.[Ugur], Ulusoy, Ö.[Özgür],
Automatic detection of salient objects and spatial relations in videos for a video database system,
IVC(26), No. 10, 1 October 2008, pp. 1384-1396.
Elsevier DOI 0804
Multimedia databases; Salient object detection and tracking; Camera focus estimation; Object labeling; Knowledge-base construction; Spatio-temporal queries BibRef

Lee, S.J.[Seung-Jin], Kim, K.[Kwanho], Kim, J.Y.[Joo-Young], Kim, M.S.[Min-Su], Yoo, H.J.[Hoi-Jun],
Familiarity based unified visual attention model for fast and robust object recognition,
PR(43), No. 3, March 2010, pp. 1116-1128.
Elsevier DOI 1001
Visual attention; Object recognition; Scene analysis BibRef

Abdollahian, G.[Golnaz], Taskiran, C.M., Pizlo, Z.[Zygmunt], Delp, E.J.[Edward J.],
Camera Motion-Based Analysis of User Generated Video,
MultMed(12), No. 1, January 2010, pp. 28-41.
IEEE DOI 1001
BibRef

Abdollahian, G.[Golnaz], Pizlo, Z.[Zygmunt], Delp, E.J.[Edward J.],
A study on the effect of camera motion on human visual attention,
ICIP08(693-696).
IEEE DOI 0810
BibRef

Zhang, W.[Wei], Wu, Q.M.J.[Q. M. Jonathan], Wang, G.H.[Guang-Hui], Yin, H.,
An Adaptive Computational Model for Salient Object Detection,
MultMed(12), No. 4, 2010, pp. 300-316.
IEEE DOI 1006
BibRef
Earlier:
Adaptive semantic Bayesian framework for image attention,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhang, W.[Wei], Wu, Q.M.J.[Q. M. Jonathan], Wang, G.H.[Guang-Hui], You, X.,
Tracking and Pairing Vehicle Headlight in Night Scenes,
ITS(13), No. 1, March 2012, pp. 140-153.
IEEE DOI 1203
BibRef
Earlier: A1, A2, A3, Only:
Vehicle Headlights Detection Using Markov Random Fields,
ACCV09(I: 169-179).
Springer DOI 0909
BibRef

Borji, A.[Ali], Ahmadabadi, M.N.[Majid Nili], Araabi, B.N.[Babak Nadjar], Hamidi, M.[Mandana],
Online learning of task-driven object-based visual attention control,
IVC(28), No. 7, July 2010, pp. 1130-1145.
Elsevier DOI 1006
BibRef
Earlier: A1, A3, A2, Only:
Learning top-down feature based attention control,
ViA08(xx-yy). 0810
Task-driven attention; Object-based attention; Top-down attention; Saliency-based model; Reinforcement learning; State space discretization BibRef

Borji, A.[Ali], Ahmadabadi, M.N.[Majid N.], Araabi, B.N.[Babak N.],
Cost-sensitive learning of top-down modulation for attentional control,
MVA(22), No. 1, January 2011, pp. 61-76.
WWW Link. 1101
BibRef

Begum, M., Karray, F., Mann, G.K.I.[George K.I.], Gosine, R.G.[Raymond G.],
A Probabilistic Model of Overt Visual Attention for Cognitive Robots,
SMC-B(40), No. 5, October 2010, pp. 1305-1318.
IEEE DOI 1003
BibRef

Yu, Y., Mann, G.K.I.[George K.I.], Gosine, R.G.[Raymond G.],
An Object-Based Visual Attention Model for Robotic Applications,
SMC-B(40), No. 5, October 2010, pp. 1398-1412.
IEEE DOI 1003
BibRef

de Silva, O., Mann, G.K.I.[George K.I.], Gosine, R.G.[Raymond G.],
Automated tuning of the nonlinear complementary filter for an Attitude Heading Reference observer,
WORV13(171-176)
IEEE DOI 1307
Kalman filters BibRef

Lee, W.F., Huang, T.H., Yeh, S.L., Chen, H.H.,
Learning-Based Prediction of Visual Attention for Video Signals,
IP(20), No. 11, November 2011, pp. 3028-3038.
IEEE DOI 1110
BibRef

Leborán, V., Garcia-Diaz, A.[Antón], Fdez-Vidal, X.R.[Xosé R.], Pardo, X.M.[Xosé M.],
Dynamic Whitening Saliency,
PAMI(39), No. 5, May 2017, pp. 893-907.
IEEE DOI 1704
Adaptation models BibRef

Garcia-Diaz, A.[Antón], Fdez-Vidal, X.R.[Xosé R.], Pardo, X.M.[Xosé M.], Dosil, R.[Raquel],
Decorrelation and Distinctiveness Provide with Human-Like Saliency,
ACIVS09(343-354).
Springer DOI 0909
BibRef
And:
Saliency Based on Decorrelation and Distinctiveness of Local Responses,
CAIP09(261-268).
Springer DOI 0909
Distinctiveness BibRef

Amano, K.[Kinjiro], Foster, D.H.[David H.], Mould, M.S.[Matthew S.], Oakley, J.P.[John P.],
Visual search in natural scenes explained by local color properties,
JOSA-A(29), No. 2, February 2012, pp. A194-A199.
WWW Link. 1202
BibRef

Dozal, L.[León], Olague, G.[Gustavo], Clemente, E.[Eddie], Sánchez, M.[Marco],
Evolving Visual Attention Programs through EVO Features,
EvoIASP12(326-335).
Springer DOI 1204
BibRef

Borji, A.[Ali], Itti, L.[Laurent],
State-of-the-Art in Visual Attention Modeling,
PAMI(35), No. 1, January 2013, pp. 185-207.
IEEE DOI 1212
BibRef
Earlier:
Exploiting local and global patch rarities for saliency detection,
CVPR12(478-485).
IEEE DOI 1208
BibRef

Borji, A.[Ali], Itti, L.[Laurent],
Human vs. Computer in Scene and Object Recognition,
CVPR14(113-120)
IEEE DOI 1409
computer vision BibRef

Borji, A.[Ali], Sihite, D.N.[Dicky N.], Itti, L.[Laurent],
What/Where to Look Next? Modeling Top-Down Visual Attention in Complex Interactive Environments,
SMCS(44), No. 5, May 2014, pp. 523-538.
IEEE DOI 1405
BibRef
Earlier:
Probabilistic learning of task-specific visual attention,
CVPR12(470-477).
IEEE DOI 1208
BibRef
Earlier:
Computational Modeling of Top-down Visual Attention in Interactive Environments,
BMVC11(xx-yy).
HTML Version. 1110
behavioural sciences computing BibRef

Peters, R.J.[Robert J.], Itti, L.[Laurent],
Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention,
CVPR07(1-8).
IEEE DOI 0706
Attention selection. BibRef

Lin, Y.W.[Yue-Wei], Tang, Y.Y.[Yuan Yan], Fang, B.[Bin], Shang, Z.W.[Zhao-Wei], Huang, Y.H.[Yong-Hui], Wang, S.[Song],
A Visual-Attention Model Using Earth Mover's Distance-Based Saliency Measurement and Nonlinear Feature Combination,
PAMI(35), No. 2, February 2013, pp. 314-328.
IEEE DOI 1301
for dynamic and static saliency maps. BibRef

Ge, S.S.[Shuzhi Sam], He, H.S.[Hong-Sheng], Zhang, Z.C.[Zheng-Chen],
Bottom-up saliency detection for attention determination,
MVA(24), No. 1, January 2013, pp. 103-116.
WWW Link. 1301
BibRef

Li, H.L.[Hong-Liang], Xu, L.F.[Lin-Feng], Liu, G.H.[Guang-Hui],
Two-layer average-to-peak ratio based saliency detection,
SP:IC(28), No. 1, January 2013, pp. 55-68.
Elsevier DOI 1301
Visual saliency; Attention model; Region detection BibRef

Hou, W.L.[Wei-Long], Gao, X.[Xinbo], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Visual saliency detection using information divergence,
PR(46), No. 10, October 2013, pp. 2658-2669.
Elsevier DOI 1306
Visual attention; Saliency detection; Independent component analysis; Bayesian surprise model BibRef

Lang, C.Y.[Cong-Yan], Feng, J.S.[Jia-Shi], Liu, G.C.[Guang-Can], Tang, J.H.[Jin-Hui], Yan, S.C.[Shui-Cheng], Luo, J.B.[Jie-Bo],
Improving Bottom-up Saliency Detection by Looking into Neighbors,
CirSysVideo(23), No. 6, 2013, pp. 1016-1028.
IEEE DOI constrained nuclear norm; saliency detection; visual attention 1307
BibRef

Gu, X.D.[Xiao-Dong], Fang, Y.[Yu], Wang, Y.Y.[Yuan-Yuan],
Attention Selection Using Global Topological Properties Based on Pulse Coupled Neural Network,
CVIU(117), No. 10, 2013, pp. 1400-1411.
Elsevier DOI 1309
Topological properties
See also Image Thinning Using Pulse Coupled Neural Network. BibRef

Le Callet, P., Niebur, E.,
Visual Attention and Applications in Multimedia Technologies,
PIEEE(101), No. 9, 2013, pp. 2058-2067.
IEEE DOI 1309
Image analysis BibRef

Gray, R., Spence, C., Ho, C., Tan, H.Z.,
Efficient Multimodal Cuing of Spatial Attention,
PIEEE(101), No. 9, 2013, pp. 2113-2122.
IEEE DOI 1309
Human factors BibRef

Xu, L.F.[Lin-Feng], Zeng, L.Y.[Liao-Yuan], Wang, Z.N.[Zheng-Ning],
Learning a Saliency Map for Fixation Prediction,
IEICE(E96-D), No. 10, October 2013, pp. 2294-2297.
WWW Link. 1310
BibRef

Nguyen, T.V., Ni, B.B.[Bing-Bing], Liu, H.R.[Hai-Rong], Xia, W.[Wei], Luo, J.B.[Jie-Bo], Kankanhalli, M., Yan, S.C.[Shui-Cheng],
Image Re-Attentionizing,
MultMed(15), No. 8, December 2013, pp. 1910-1919.
IEEE DOI 1402
Markov processes. Attract human attention. BibRef

Nguyen, T.V.[Tam V.], Zhao, Q.[Qi], Yan, S.C.[Shui-Cheng],
Attentive Systems: A Survey,
IJCV(126), No. 1, January 2018, pp. 86-110.
Springer DOI 1801
Survey, Attention. BibRef

Andrushia, A.D.[A. Diana], Thangarajan, R., Sebastian, G.[Greeshma],
Performance analysis on visual attention using spiking and oscillatory neural model,
IJCVR(3), No. 3, 2013, pp. 293-307.
DOI Link 1402
BibRef

Wang, W.N.[Wei-Ning], Cai, D.[Dong], Xu, X.M.[Xiang-Min], Liew, A.W.C.[Alan Wee-Chung],
Visual saliency detection based on region descriptors and prior knowledge,
SP:IC(29), No. 3, 2014, pp. 424-433.
Elsevier DOI 1403
Visual saliency BibRef

Kim, H., Lee, S.H.[Sang-Hoon], Bovik, A.C.,
Saliency Prediction on Stereoscopic Videos,
IP(23), No. 4, April 2014, pp. 1476-1490.
IEEE DOI 1404
image sensors. Lowlevel features and high-level scenes in videos. BibRef

Arnay, R.[Rafael], Acosta, L.[Leopoldo],
Contour-based focus of attention mechanism to speed up object detection and labeling in 3D scenes,
IVC(32), No. 5, 2014, pp. 303-320.
Elsevier DOI 1404
3D contour-based features BibRef

Luo, Y., Jiang, M., Wong, Y., Zhao, Q.,
Multi-Camera Saliency,
PAMI(37), No. 10, October 2015, pp. 2057-2070.
IEEE DOI 1509
Cameras BibRef

Jiang, M.[Ming], Xu, J.[Juan], Zhao, Q.[Qi],
Saliency in Crowd,
ECCV14(VII: 17-32).
Springer DOI 1408
where people look in a crowded scene. BibRef

Shen, C.Y.[Cheng-Yao], Zhao, Q.[Qi],
Webpage Saliency,
ECCV14(VII: 33-46).
Springer DOI 1408
BibRef

Qiao, H., Xi, X., Li, Y., Wu, W., Li, F.,
Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment,
Cyber(45), No. 11, November 2015, pp. 2612-2624.
IEEE DOI 1511
Biological system modeling BibRef

Qiao, H., Li, Y., Li, F., Xi, X., Wu, W.,
Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning,
Cyber(46), No. 10, October 2016, pp. 2335-2347.
IEEE DOI 1610
cognition BibRef

Engelke, U.[Ulrich], Le Callet, P.[Patrick],
Perceived interest and overt visual attention in natural images,
SP:IC(39, Part B), No. 1, 2015, pp. 386-404.
Elsevier DOI 1512
Overt visual attention BibRef

Hirayama, T.[Takatsugu], Ohira, T.[Toshiya], Mase, K.[Kenji],
Top-Down Visual Attention Estimation Using Spatially Localized Activation Based on Linear Separability of Visual Features,
IEICE(E98-D), No. 12, December 2015, pp. 2308-2316.
WWW Link. 1601
BibRef

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IEEE DOI 1601
Computational modeling BibRef

Mateescu, V.A., Bajic, I.V.,
Visual Attention Retargeting,
MultMedMag(23), No. 1, January 2016, pp. 82-91.
IEEE DOI 1603
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Wang, J., Borji, A., Kuo, C.C.J.[C. C. Jay], Itti, L.,
Learning a Combined Model of Visual Saliency for Fixation Prediction,
IP(25), No. 4, April 2016, pp. 1566-1579.
IEEE DOI 1604
image fusion BibRef

Feng, M., Borji, A., Lu, H.,
Fixation prediction with a combined model of bottom-up saliency and vanishing point,
WACV16(1-7)
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A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions,
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image processing BibRef

Lahrache, S., El Ouazzani, R., El Qadi, A.,
Bag-of-features for image memorability evaluation,
IET-CV(10), No. 6, 2016, pp. 577-584.
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computer vision BibRef

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Visual Focus of Attention Estimation With Unsupervised Incremental Learning,
CirSysVideo(26), No. 12, December 2016, pp. 2264-2272.
IEEE DOI 1612
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Unsupervised online learning of visual focus of attention,
AVSS13(25-30)
IEEE DOI 1311
Clustering algorithms BibRef

Gao, G.Y.[Guang-Yu], Han, C.[Cen], Ma, K.[Kun], Liu, C.H.[Chi Harold], Ding, G.Y.[Gang-Yi], Liu, E.[Erwu],
Optimal feature combination analysis for crowd saliency prediction,
JVCIR(50), No. 1, 2018, pp. 1-8.
Elsevier DOI 1712
Predicting where people look at in crowd scene. Crowd, Saliency, Random forest, Visual attention, Face detection BibRef

Wang, W., Shen, J.,
Deep Visual Attention Prediction,
IP(27), No. 5, May 2018, pp. 2368-2378.
IEEE DOI 1804
learning (artificial intelligence), neural nets, object detection, CNN-based attention models, saliency detection BibRef

Zheng, Z., Zhao, H., Swanson, A.R., Weitlauf, A.S., Warren, Z.E., Sarkar, N.,
Design, Development, and Evaluation of a Noninvasive Autonomous Robot-Mediated Joint Attention Intervention System for Young Children With ASD,
HMS(48), No. 2, April 2018, pp. 125-135.
IEEE DOI 1804
Cameras, Head, Monitoring, Protocols, Robot kinematics, Robot sensing systems, robot-assisted intervention BibRef

Alshawi, T.[Tariq], Long, Z.L.[Zhi-Ling], Al Regib, G.[Ghassan],
Unsupervised Uncertainty Estimation Using Spatiotemporal Cues in Video Saliency Detection,
IP(27), No. 6, June 2018, pp. 2818-2827.
IEEE DOI 1804
estimation theory, image colour analysis, image motion analysis, object detection, spatiotemporal phenomena, visual attention BibRef

Liu, Q., Yang, Y., Li, P., Li, B.,
A robust 3D visual saliency computation model for human fixation prediction of stereoscopic videos,
VCIP17(1-4)
IEEE DOI 1804
feature extraction, image colour analysis, image fusion, image motion analysis, image resolution, image texture, Saliency Computational Model BibRef

Assens, M.[Marc], Giro-i-Nieto, X.[Xavier], McGuinness, K.[Kevin], O'Connor, N.E.[Noel E.],
Scanpath and saliency prediction on 360 degree images,
SP:IC(69), 2018, pp. 8-14.
Elsevier DOI 1811
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SaltiNet: Scan-Path Prediction on 360 Degree Images Using Saliency Volumes,
Egocentric17(2331-2338)
IEEE DOI 1802
Code, Saliency.
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Cyr, A.[André], Thériault, F.[Frédéric],
Bio-inspired visual attention process using spiking neural networks controlling a camera,
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Fang, Y., Zhang, C., Huang, H., Lei, J.,
Visual Attention Prediction for Stereoscopic Video by Multi-Module Fully Convolutional Network,
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IEEE DOI 1909
Visualization, Stereo image processing, Feature extraction, Computational modeling, Object detection, fully convolutional network BibRef

Tünnermann, J.[Jan], Born, C.[Christian], Mertsching, B.[Bärbel],
Saliency From Growing Neural Gas: Learning Pre-Attentional Structures for a Flexible Attention System,
IP(28), No. 11, November 2019, pp. 5296-5307.
IEEE DOI 1909
Task analysis, Biological system modeling, Modeling, Image color analysis, Visualization, Object detection, Saliency, growing neural gas BibRef

Mahdi, A.[Ali], Qin, J.[Jun],
An extensive evaluation of deep features of convolutional neural networks for saliency prediction of human visual attention,
JVCIR(65), 2019, pp. 102662.
Elsevier DOI 1912
Convolutional neural networks, Feature maps, Human fixation prediction, Saliency map, Transfer learning BibRef

Min, X., Zhai, G., Zhou, J., Zhang, X., Yang, X., Guan, X.,
A Multimodal Saliency Model for Videos With High Audio-Visual Correspondence,
IP(29), 2020, pp. 3805-3819.
IEEE DOI 2002
Audio-visual attention, visual attention, multimodal, saliency, attention fusion BibRef

Zanca, D.[Dario], Melacci, S.[Stefano], Gori, M.[Marco],
Gravitational Laws of Focus of Attention,
PAMI(42), No. 12, December 2020, pp. 2983-2995.
IEEE DOI 2011
Visualization, Computational modeling, Mathematical model, Task analysis, Brightness, Predictive modeling, Gravity, gravitational laws BibRef

Li, K., Wu, Z., Peng, K.C., Ernst, J., Fu, Y.,
Guided Attention Inference Network,
PAMI(42), No. 12, December 2020, pp. 2996-3010.
IEEE DOI 2011
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Tell Me Where to Look: Guided Attention Inference Network,
CVPR18(9215-9223)
IEEE DOI 1812
Neural networks, Semantics, Visualization, Image segmentation, Supervised learning, Training data, biased data. Task analysis, Training BibRef

Jiang, L.[Lai], Xu, M.[Mai], Wang, Z.L.[Zu-Lin], Sigal, L.[Leonid],
DeepVS2.0: A Saliency-Structured Deep Learning Method for Predicting Dynamic Visual Attention,
IJCV(129), No. 1, January 2021, pp. 203-224.
Springer DOI 2101
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Khandelwal, S., Sigal, L.,
AttentionRNN: A Structured Spatial Attention Mechanism,
ICCV19(3424-3433)
IEEE DOI 2004
convolutional neural nets, feedforward neural nets, learning (artificial intelligence), Computational modeling BibRef

Yuan, Y.[Yuan], Ning, H.L.[Hai-Long], Lu, X.Q.[Xiao-Qiang],
Bio-Inspired Representation Learning for Visual Attention Prediction,
Cyber(51), No. 7, July 2021, pp. 3562-3575.
IEEE DOI 2106
Feature extraction, Semantics, Visualization, Object detection, Cybernetics, Deep learning, Bio-inspired, visual attention prediction (VAP) BibRef

Lai, Q.X.[Qiu-Xia], Khan, S.[Salman], Nie, Y.W.[Yong-Wei], Sun, H.[Hanqiu], Shen, J.B.[Jian-Bing], Shao, L.[Ling],
Understanding More About Human and Machine Attention in Deep Neural Networks,
MultMed(23), 2021, pp. 2086-2099.
IEEE DOI 2107
Task analysis, Visualization, Neural networks, Object segmentation, Image recognition, Reliability, deep learning BibRef

Shi, X.[Xiang], Yang, Y.[You], Liu, Q.[Qiong],
I Understand You: Blind 3D Human Attention Inference from the Perspective of Third-Person,
IP(30), 2021, pp. 6212-6225.
IEEE DOI 2107
Feature extraction, Estimation, Faces, Solid modeling, Visualization, Cameras, Human attention inference, scene understanding, Long-Short-Term-Memory (LSTM) BibRef

Li, A.[Aoqi], Chen, Z.Z.[Zhen-Zhong],
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Elsevier DOI 2109
Visual attention, Image saliency, Semantic attributes, Object importance BibRef

Cheng, D.Q.[De-Qiang], Liu, R.H.[Rui-Hang], Li, J.H.[Jia-Han], Liang, S.[Song], Kou, Q.Q.[Qi-Qi], Zhao, K.[Kai],
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IVC(114), 2021, pp. 104267.
Elsevier DOI 2109
Saliency prediction, Convolutional neural networks, Human eye fixations, Deep learning BibRef

Nan, Z.X.[Zhi-Xiong], Jiang, J.J.[Jing-Jing], Gao, X.F.[Xiao-Feng], Zhou, S.P.[San-Ping], Zuo, W.L.[Wei-Liang], Wei, P.[Ping], Zheng, N.N.[Nan-Ning],
Predicting Task-Driven Attention via Integrating Bottom-Up Stimulus and Top-Down Guidance,
IP(30), 2021, pp. 8293-8305.
IEEE DOI 2110
Task analysis, Predictive models, Feature extraction, Image color analysis, Visualization, Computer architecture, task-driven BibRef

Xia, C.[Chen], Han, J.W.[Jun-Wei], Zhang, D.W.[Ding-Wen],
Evaluation of Saccadic Scanpath Prediction: Subjective Assessment Database and Recurrent Neural Network Based Metric,
PAMI(43), No. 12, December 2021, pp. 4378-4395.
IEEE DOI 2112
Measurement, Predictive models, Visualization, Feature extraction, Computational modeling, Visual databases, Visual attention, semantic hashing BibRef

Beelders, T.[Tanya], Dollman, G.[Gavin],
Virtual Prospecting in Paleontology Using a Drone-Based Orthomosaic Map: An Eye Movement Analysis,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
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Zhang, H.[Hao], Peng, G.Q.[Guo-Qin], Wu, Z.C.[Zhi-Chao], Gong, J.[Jian], Xu, D.[Dan], Shi, H.Z.[Hong-Zhen],
MAM: A multipath attention mechanism for image recognition,
IET-IPR(16), No. 3, 2022, pp. 691-702.
DOI Link 2202
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Long, X.[Xiang], de Melo, G.[Gerard], He, D.L.[Dong-Liang], Li, F.[Fu], Chi, Z.Z.[Zhi-Zhen], Wen, S.[Shilei], Gan, C.[Chuang],
Purely Attention Based Local Feature Integration for Video Classification,
PAMI(44), No. 4, April 2022, pp. 2140-2154.
IEEE DOI 2203
Feature extraction, Convolution, Computational modeling, Plugs, Task analysis, Video classification, action recognition, computer vision BibRef

Long, X., Gan, C., de Melo, G., Wu, J., Liu, X., Wen, S.,
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification,
CVPR18(7834-7843)
IEEE DOI 1812
Feature extraction, Task analysis, Recurrent neural networks, Computational modeling, Optical imaging, Optical network units BibRef

Ding, G.Q.[Guan-Qun], Imamoglu, N.[Nevrez], Caglayan, A.[Ali], Murakawa, M.[Masahiro], Nakamura, R.[Ryosuke],
SalFBNet: Learning pseudo-saliency distribution via feedback convolutional networks,
IVC(120), 2022, pp. 104395.
Elsevier DOI 2204
Better learn distinguishable eye-fixation-based features. Feedback networks, Human gaze, Pseudo-saliency, Selective fixation and non-fixation error BibRef

Lateef, F.[Fahad], Kas, M.[Mohamed], Ruichek, Y.[Yassine],
Saliency Heat-Map as Visual Attention for Autonomous Driving Using Generative Adversarial Network (GAN),
ITS(23), No. 6, June 2022, pp. 5360-5373.
IEEE DOI 2206
Visualization, Vehicles, Generative adversarial networks, Computational modeling, Autonomous vehicles, Predictive models, scene understanding BibRef

Zhu, Y.C.[Yu-Cheng], Zhai, G.T.[Guang-Tao], Yang, Y.[Yiwei], Duan, H.Y.[Hui-Yu], Min, X.K.[Xiong-Kuo], Yang, X.K.[Xiao-Kang],
Viewing Behavior Supported Visual Saliency Predictor for 360 Degree Videos,
CirSysVideo(32), No. 7, July 2022, pp. 4188-4201.
IEEE DOI 2207
Visualization, Videos, Feature extraction, Head, Predictive models, Solid modeling, Magnetic heads, Virtual reality, visual attention, head and eye movement BibRef

Li, T.[Tao], Ma, J.W.[Jin-Wen],
Attention Mechanism Based Mixture of Gaussian Processes,
PRL(161), 2022, pp. 130-136.
Elsevier DOI 2209
Gaussian processes, Attention, Mixture model BibRef

Gomez, T.[Tristan], Ling, S.[Suiyi], Fréour, T.[Thomas], Mouchère, H.[Harold],
BR-NPA: A non-parametric high-resolution attention model to improve the interpretability of attention,
PR(132), 2022, pp. 108927.
Elsevier DOI 2209
Deep learning, Interpretability, Spatial attention, Resolution, Non-parametric BibRef

Veiga, T.[Tiago], Renoux, J.[Jennifer],
From Reactive to Active Sensing: A Survey on Information Gathering in Decision-Theoretic Planning,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link 2309
information gathering, Decision-theoretic planning, active sensing BibRef

Ma, Y.W.[Yi-Wei], Ji, J.Y.[Jia-Yi], Sun, X.S.[Xiao-Shuai], Zhou, Y.[Yiyi], Wu, Y.J.[Yong-Jian], Huang, F.Y.[Fei-Yue], Ji, R.R.[Rong-Rong],
Knowing What it is: Semantic-Enhanced Dual Attention Transformer,
MultMed(25), 2023, pp. 3723-3736.
IEEE DOI Code:
WWW Link. 2310
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Lu, E.H.C.[Eric Hsueh-Chan], Lin, Y.R.[You-Ru],
A Self-Attention Model for Next Location Prediction Based on Semantic Mining,
IJGI(12), No. 10, 2023, pp. 420.
DOI Link 2311
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Wang, S.[Shuo], Wu, Z.H.[Zhi-Hao], Hu, X.B.[Xiao-Bo], Lin, Y.F.[You-Fang], Lv, K.[Kai],
Skill-Based Hierarchical Reinforcement Learning for Target Visual Navigation,
MultMed(25), 2023, pp. 8920-8932.
IEEE DOI 2312
find a target object. BibRef

Ge, C.J.[Chong-Jian], Song, Y.B.[Yi-Bing], Ma, C.[Chao], Qi, Y.[Yuankai], Luo, P.[Ping],
Rethinking Attentive Object Detection via Neural Attention Learning,
IP(33), 2024, pp. 1726-1739.
IEEE DOI 2403
Object detection, Proposals, Detectors, Training, Visualization, Convolutional neural networks, Head, Object detection, neural attention learning BibRef

Han, G.[Gaoge], Huang, S.L.[Shao-Li], Zhao, F.[Fang], Tang, J.L.[Jing-Lei],
SIAM: A parameter-free, Spatial Intersection Attention Module,
PR(153), 2024, pp. 110509.
Elsevier DOI 2405
3-D attention module, Parameter-free, Convolutional neural networks BibRef

Bai, X.[Xiao], Zhang, P.C.[Peng-Cheng], Yu, X.H.[Xiao-Han], Zheng, J.[Jin], Hancock, E.R.[Edwin R.], Zhou, J.[Jun], Gu, L.[Lin],
Learning From Human Attention for Attribute-Assisted Visual Recognition,
PAMI(46), No. 12, December 2024, pp. 11152-11167.
IEEE DOI 2411
Use attention/gaze in recognition. Feature extraction, Visualization, Prototypes, Data models, Location awareness, Adaptation models, Zero-shot learning, zero-shot learning BibRef

Yang, Z.B.[Zhi-Bo], Mondal, S.[Sounak], Ahn, S.[Seoyoung], Xue, R.[Ruoyu], Zelinsky, G.[Gregory], Hoai, M.[Minh], Samaras, D.[Dimitris],
Unifying Top-Down and Bottom-Up Scanpath Prediction Using Transformers,
CVPR24(1683-1693)
IEEE DOI Code:
WWW Link. 2410
Solid modeling, Visualization, Computational modeling, Computer architecture, Predictive models, Transformers, Retina, Free Viewing BibRef

Kerkouri, M.A.[Mohamed Amine], Tliba, M.[Marouane], Chetouani, A.[Aladine], Bruno, A.[Alessandro],
An Inter-Observer Consistent Deep Adversarial Training for Visual Scanpath Prediction,
ICIP23(2595-2599)
IEEE DOI 2312
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Shi, B.F.[Bai-Feng], Darrell, T.J.[Trevor J.], Wang, X.[Xin],
Top-Down Visual Attention from Analysis by Synthesis,
CVPR23(2102-2112)
IEEE DOI 2309
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Mondal, S.[Sounak], Yang, Z.B.[Zhi-Bo], Ahn, S.[Seoyoung], Samaras, D.[Dimitris], Zelinsky, G.[Gregory], Hoai, M.[Minh],
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention,
CVPR23(1441-1450)
IEEE DOI 2309
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Paula, B.[Beatriz], Moreno, P.[Plinio],
Learning to Search for and Detect Objects in Foveal Images Using Deep Learning,
IbPRIA23(223-237).
Springer DOI 2307
Evaluate fixation points. BibRef

Zhong, S.S.[Shan-Shan], Wen, W.[Wushao], Qin, J.H.[Jing-Hui],
SPEM: Self-adaptive Pooling Enhanced Attention Module for Image Recognition,
MMMod23(II: 41-53).
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Kadner, F.[Florian], Thomas, T.[Tobias], Hoppe, D.[David], Rothkopf, C.A.[Constantin A.],
Improving saliency models' predictions of the next fixation with humans' intrinsic cost of gaze shifts,
WACV23(2103-2113)
IEEE DOI 2302
Costs, Heuristic algorithms, Computational modeling, Decision making, Predictive models, Benchmark testing, Applications: Psychology and cognitive science BibRef

Wang, H.Y.[Han-Yu], Gupta, K.[Kamal], Davis, L.S.[Larry S.], Shrivastava, A.[Abhinav],
Neural Space-Filling Curves,
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Zhang, K.[Kepei], Lu, M.Q.[Mei-Qi], Lu, Z.[Zheng], Zhang, X.T.[Xue-Tao],
Scanpath Prediction Via Semantic Representation of the Scene,
ICIP22(1976-1980)
IEEE DOI 2211
Object instances and backgrounds in scenes, and uses the attention mechanism to learn the semantic correlation between objects. Visualization, Image segmentation, Graphical models, Correlation, Semantics, Reinforcement learning, Predictive models, Inverse Reinforcement Learning BibRef

Xu, Y.X.[Yu-Xiao], Huo, Y.K.[Yong-Kai], Song, Y.[Yukai],
Panoramic Viewport Prediction Relying on Emotional Attention Map,
ICIP22(1751-1755)
IEEE DOI 2211
Visualization, Estimation, Immersive experience, Predictive models, Benchmark testing, Trajectory, Panoramic video, Emotional attention BibRef

Petryk, S.[Suzanne], Dunlap, L.[Lisa], Nasseri, K.[Keyan], Gonzalez, J.[Joseph], Darrell, T.J.[Trevor J.], Rohrbach, A.[Anna],
On Guiding Visual Attention with Language Specification,
CVPR22(18071-18081)
IEEE DOI 2210
Visualization, Image recognition, Training data, Feature extraction, Numerical models, Visual reasoning BibRef

Chen, Q.[Qiang], Wu, Q.[Qiman], Wang, J.[Jian], Hu, Q.H.[Qing-Hao], Hu, T.[Tao], Ding, E.[Errui], Cheng, J.[Jian], Wang, J.D.[Jing-Dong],
MixFormer: Mixing Features across Windows and Dimensions,
CVPR22(5239-5249)
IEEE DOI 2210
Code, Attention.
WWW Link. Couplings, Codes, Convolution, Bidirectional control, Transformers, Pattern recognition, Recognition: detection, categorization, retrieval BibRef

Long, F.C.[Fu-Chen], Qiu, Z.F.[Zhao-Fan], Pan, Y.W.[Ying-Wei], Yao, T.[Ting], Luo, J.B.[Jie-Bo], Mei, T.[Tao],
Stand-Alone Inter-Frame Attention in Video Models,
CVPR22(3182-3191)
IEEE DOI 2210
Code, Attention.
WWW Link. Convolutional codes, Weight measurement, Deep learning, Solid modeling, Computational modeling, Transformers, Video analysis and understanding BibRef

Arar, M.[Moab], Shamir, A.[Ariel], Bermano, A.H.[Amit H.],
Learned Queries for Efficient Local Attention,
CVPR22(10831-10842)
IEEE DOI 2210
Convolutional codes, Image synthesis, Memory management, Transformers, Complexity theory, retrieval, Recognition: detection BibRef

Yang, Z.B.[Zhi-Bo], Mondal, S.[Sounak], Ahn, S.[Seoyoung], Zelinsky, G.[Gregory], Hoai, M.[Minh], Samaras, D.[Dimitris],
Target-Absent Human Attention,
ECCV22(IV:52-68).
Springer DOI 2211
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Chen, Y.P.[Yu-Pei], Yang, Z.B.[Zhi-Bo], Chakraborty, S.[Souradeep], Mondal, S.[Sounak], Ahn, S.[Seoyoung], Samaras, D.[Dimitris], Hoai, M.[Minh], Zelinsky, G.[Gregory],
Characterizing Target-absent Human Attention,
Gaze22(5027-5036)
IEEE DOI 2210
Conferences, Object detection, Machine learning, Detectors, Predictive models, Behavioral sciences BibRef

Jha, A.[Abhishek], Seifi, S.[Soroush], Tuytelaars, T.[Tinne],
SimGlim: Simplifying glimpse based active visual reconstruction,
WACV23(269-278)
IEEE DOI 2302
BibRef
Earlier: A2, A1, A3:
Glimpse-Attend-and-Explore: Self-Attention for Active Visual Exploration,
ICCV21(16117-16126)
IEEE DOI 2203
Visualization, Reconstruction algorithms, Predictive models, Transformers, Data models, Task analysis, image and video synthesis. Representation learning, Adaptation models, Uncertainty, Reinforcement learning, grouping and shape BibRef

Leyva, R.[Roberto], Sanchez, V.[Victor],
Video Memorability Prediction Via Late Fusion of Deep Multi-Modal Features,
ICIP21(2488-2492)
IEEE DOI 2201
Visualization, Fuses, Social networking (online), Computational modeling, Neural networks, Feature extraction, fusion BibRef

Chen, Q.P.[Qi-Pin], Shi, Z.Y.[Zhen-Yu], Zuo, Z.[Zhen], Fu, J.M.[Jin-Miao], Sun, Y.[Yi],
Two-Stream Hybrid Attention Network for Multimodal Classification,
ICIP21(359-363)
IEEE DOI 2201
Image processing, multimodal classification, hybrid attention mechanism BibRef

Martins, P.H.[Pedro Henrique], Niculae, V.[Vlad], Marinho, Z.[Zita], Martins, A.F.T.[André F. T.],
Sparse and Structured Visual Attention,
ICIP21(379-383)
IEEE DOI 2201
Visualization, Image processing, Knowledge discovery, Task analysis, Attention, Structured Sparsity, Total Variation BibRef

Farinhas, A.[António], Martins, A.F.T.[André F. T.], Aguiar, P.M.Q.[Pedro M. Q.],
Multimodal Continuous Visual Attention Mechanisms,
VIPriors21(1047-1056)
IEEE DOI 2112
Jacobian matrices, Visualization, Shape, Computational modeling, Neural networks, MIMICs BibRef

Siegfried, R.[Rémy], Odobez, J.M.[Jean-Marc],
Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets,
Gaze21(3147-3155)
IEEE DOI 2109
Deep learning, Training, Visualization, TV, Estimation, Usability BibRef

Shen, Z.R.[Zhuo-Ran], Zhang, M.Y.[Ming-Yuan], Zhao, H.Y.[Hai-Yu], Yi, S.[Shuai], Li, H.S.[Hong-Sheng],
Efficient Attention: Attention with Linear Complexities,
WACV21(3530-3538)
IEEE DOI 2106
Computational modeling, Memory management, Estimation, Object detection, Detectors BibRef

Dai, Y.M.[Yi-Mian], Oehmcke, S.[Stefan], Gieseke, F.[Fabian], Wu, Y.Q.[Yi-Quan], Barnard, K.[Kobus],
Attention as Activation,
ICPR21(9156-9163)
IEEE DOI 2105
Aggregates, Performance gain BibRef

Krishna, O.[Onkar], Irie, G.[Go], Kawanishi, T.[Takahito], Kashino, K.[Kunio], Aizawa, K.[Kiyoharu],
Translating Adult's Focus of Attention to Elderly's,
ICPR21(563-568)
IEEE DOI 2105
Training, Legged locomotion, Computational modeling, Senior citizens, Estimation, Predictive models, Observers BibRef

Efremova, N., Hajimirza, N., Bassett, D., Thomaz, F.,
Understanding consumer attention on mobile devices,
FG20(919-919)
IEEE DOI 2102
Mobile handsets, Social networking (online), Generators, Visualization, Webcams, Glass, Face recognition, attention, mobile BibRef

Ajmal, A.[Aisha], Al-Sahaf, H.[Harith], Hollitt, C.[Christopher],
Salient Motion Features for Visual Attention Models,
IVCNZ20(1-6)
IEEE DOI 2012
Visualization, Image color analysis, Computational modeling, Estimation, Color, Feature extraction, Optical flow, HSV BibRef

Mejjati, Y.A.[Youssef A.], Gomez, C.F.[Celso F.], Kim, K.I.[Kwang In], Shechtman, E.[Eli], Bylinskii, Z.[Zoya],
Look Here! A Parametric Learning Based Approach to Redirect Visual Attention,
ECCV20(XXIII:343-361).
Springer DOI 2011
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Uzkent, B., Ermon, S.,
Learning When and Where to Zoom With Deep Reinforcement Learning,
CVPR20(12342-12351)
IEEE DOI 2008
Task analysis, Spatial resolution, Training, Random variables, Satellites BibRef

Yang, Z., Huang, L., Chen, Y., Wei, Z., Ahn, S., Zelinsky, G., Samaras, D., Hoai, M.,
Predicting Goal-Directed Human Attention Using Inverse Reinforcement Learning,
CVPR20(190-199)
IEEE DOI 2008
Visualization, Task analysis, Predictive models, Search problems, Learning (artificial intelligence), Computational modeling, Context modeling BibRef

Wang, L.Z.[Le-Zi], Wu, Z.Y.[Zi-Yan], Karanam, S.[Srikrishna], Peng, K.C.[Kuan-Chuan], Singh, R.V.[Rajat Vikram], Liu, B.[Bo], Metaxas, D.N.[Dimitris N.],
Sharpen Focus: Learning With Attention Separability and Consistency,
ICCV19(512-521)
IEEE DOI 2004
feature extraction, image classification, image representation, learning (artificial intelligence), neural nets, Benchmark testing BibRef

Abolghasemi, P.[Pooya], Mazaheri, A.[Amir], Shah, M.[Mubarak], Boloni, L.[Ladislau],
Pay Attention! - Robustifying a Deep Visuomotor Policy Through Task-Focused Visual Attention,
CVPR19(4249-4257).
IEEE DOI 2002
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Liu, X.H.[Xi-Hui], Wang, Z.H.[Zi-Hao], Shao, J.[Jing], Wang, X.G.[Xiao-Gang], Li, H.S.[Hong-Sheng],
Improving Referring Expression Grounding With Cross-Modal Attention-Guided Erasing,
CVPR19(1950-1959).
IEEE DOI 2002
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Guo, H.[Hao], Zheng, K.[Kang], Fan, X.C.[Xiao-Chuan], Yu, H.K.[Hong-Kai], Wang, S.[Song],
Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification,
CVPR19(729-739).
IEEE DOI 2002
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Cuculo, V.[Vittorio], d'Amelio, A.[Alessandro], Grossi, G.[Giuliano], Lanzarotti, R.[Raffaella],
Worldly Eyes on Video: Learnt vs. Reactive Deployment of Attention to Dynamic Stimuli,
CIAP19(I:128-138).
Springer DOI 1909
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Fajtl, J., Argyriou, V., Monekosso, D., Remagnino, P.,
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CVPR18(6363-6372)
IEEE DOI 1812
Visualization, Feature extraction, Estimation, Task analysis, Neural networks, Training BibRef

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Active Fixation Control to Predict Saccade Sequences,
CVPR18(3184-3193)
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Visualization, Brain modeling, Computational modeling, Object detection, Search problems BibRef

Fan, H.Q.[Hao-Qi], Zhou, J.T.[Jia-Tong],
Stacked Latent Attention for Multimodal Reasoning,
CVPR18(1072-1080)
IEEE DOI 1812
Cognition, Task analysis, Computational modeling, Visualization, Computer architecture, Stacking, Knowledge discovery BibRef

Wei, P., Liu, Y., Shu, T., Zheng, N., Zhu, S.,
Where and Why are They Looking? Jointly Inferring Human Attention and Intentions in Complex Tasks,
CVPR18(6801-6809)
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Task analysis, Videos, Feature extraction, Skeleton, Bridges BibRef

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ICIP17(3745-3749)
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Computational modeling, Kernel, Mathematical model, Observers, Predictive models, Semantics, Visualization, individuality, visual attention BibRef

Weibel, J.B., Tan, H.L., Lu, S.,
An integrated approach to visual attention modelling using spatial-temporal saliency and objectness,
ICIP17(440-444)
IEEE DOI 1803
Computational modeling, Histograms, Integrated optics, Optical imaging, Sun, Visualization BibRef

Kümmerer, M., Wallis, T.S.A., Gatys, L.A., Bethge, M.,
Understanding Low- and High-Level Contributions to Fixation Prediction,
ICCV17(4799-4808)
IEEE DOI 1802
feature extraction, image classification, neural nets, object detection, object recognition, Predictive models BibRef

Li, Z., Yang, Y., Liu, X., Zhou, F., Wen, S., Xu, W.,
Dynamic Computational Time for Visual Attention,
CEFR-LCV17(1199-1209)
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Adaptation models, Computational modeling, Image recognition, Neural networks, Random access memory, Training, Visualization BibRef

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Going from Image to Video Saliency: Augmenting Image Salience with Dynamic Attentional Push,
CVPR18(7501-7511)
IEEE DOI 1812
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Attentional Push: A Deep Convolutional Network for Augmenting Image Salience with Shared Attention Modeling in Social Scenes,
CVPR17(3472-3481)
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Visualization, Computational modeling, Spatiotemporal phenomena, Color, Dynamics, Fuses, Optical imaging. Feature extraction, Head, Neural networks, Predictive models, Training BibRef

Kahou, S.E.[Samira Ebrahimi], Michalski, V.[Vincent], Memisevic, R.[Roland], Pal, C.[Christopher], Vincent, P.[Pascal],
RATM: Recurrent Attentive Tracking Model,
MotionRep17(1613-1622)
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Computational modeling, Feature extraction, Proposals, Recurrent neural networks, Standards, Training BibRef

Wang, J., Tavakoli, H.R.[Hamed R.], Laaksonen, J.[Jorma],
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DeepLearn-T17(2225-2232)
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VCIP16(1-4)
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Rahman, I.M.H., Hollitt, C., Zhang, M.,
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ICVNZ16(1-6)
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VCIP16(1-4)
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VCIP15(1-4)
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Adaptation models BibRef

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ICCV15(1089-1097)
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Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Human Attention, Gaze, Eye Tracking .