Top-Down Neural Attention by Excitation Backprop
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Zhe L. Lin | Stan Sclaroff | Jianming Zhang | Jonathan Brandt | Sarah Adel Bargal | Zhe Lin | Xiaohui Shen | S. Sclaroff | Jonathan Brandt | Xiaohui Shen | Jianming Zhang
[1] John G. Kemeny,et al. Finite Markov Chains. , 1960 .
[2] A. Treisman,et al. A feature-integration theory of attention , 1980, Cognitive Psychology.
[3] S Ullman,et al. Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.
[4] D C Van Essen,et al. Shifter circuits: a computational strategy for dynamic aspects of visual processing. , 1987, Proceedings of the National Academy of Sciences of the United States of America.
[5] J. Wolfe,et al. Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.
[6] R. Desimone,et al. Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.
[7] John K. Tsotsos,et al. Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..
[8] E. Niebur,et al. Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention , 1996, Journal of Cognitive Neuroscience.
[9] R. Desimone. Visual attention mediated by biased competition in extrastriate visual cortex. , 1998, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[10] J. Wolfe,et al. Changing your mind: on the contributions of top-down and bottom-up guidance in visual search for feature singletons. , 2003, Journal of experimental psychology. Human perception and performance.
[11] B. Nordstrom. FINITE MARKOV CHAINS , 2005 .
[12] D. Heeger,et al. The Normalization Model of Attention , 2009, Neuron.
[13] S. Kastner,et al. Top-down and bottom-up mechanisms in biasing competition in the human brain , 2009, Vision Research.
[14] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[15] L. Itti,et al. Mechanisms of top-down attention , 2011, Trends in Neurosciences.
[16] Tamás D. Gedeon,et al. Collecting Large, Richly Annotated Facial-Expression Databases from Movies , 2012, IEEE MultiMedia.
[17] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[18] Ronan Collobert,et al. Recurrent Convolutional Neural Networks for Scene Parsing , 2013, ArXiv.
[19] Matthieu Guillaumin,et al. ImageNet Auto-Annotation with Segmentation Propagation , 2014, International Journal of Computer Vision.
[20] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[21] Andrew Zisserman,et al. Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.
[22] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[23] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[24] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[25] Jonathan T. Barron,et al. Multiscale Combinatorial Grouping , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[26] Bolei Zhou,et al. Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.
[27] C. Lawrence Zitnick,et al. Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.
[28] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[29] Svetlana Lazebnik,et al. Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[30] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[31] Geoffrey Zweig,et al. From captions to visual concepts and back , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Alexander Binder,et al. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.
[33] Hod Lipson,et al. Understanding Neural Networks Through Deep Visualization , 2015, ArXiv.
[34] Wei Xu,et al. Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[35] Trevor Darrell,et al. Constrained Convolutional Neural Networks for Weakly Supervised Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[36] Bolei Zhou,et al. Object Detectors Emerge in Deep Scene CNNs , 2014, ICLR.
[37] Ronan Collobert,et al. From image-level to pixel-level labeling with Convolutional Networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] George Papandreou,et al. Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation , 2015, ArXiv.
[40] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[41] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[42] Ivan Laptev,et al. Is object localization for free? - Weakly-supervised learning with convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Tal Hassner,et al. Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns , 2015, ICMI.
[44] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Dragomir Anguelov,et al. Self-taught object localization with deep networks , 2014, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).
[47] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[48] Andrew Zisserman,et al. Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video , 2017, MICCAI.
[49] Davide Modolo,et al. Do Semantic Parts Emerge in Convolutional Neural Networks? , 2016, International Journal of Computer Vision.
[50] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[51] Andrea Vedaldi,et al. Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[52] Andrew Zisserman,et al. SpineNet: Automated classification and evidence visualization in spinal MRIs , 2017, Medical Image Anal..