Fetching the paper…
Reading the bibliography…
We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps.
Finite Markov chains
Kemeny, J.G., Snell, J.L., et al.: · 1960
Earlier work this paper cites.
A feature-integration theory of attention
Treisman, A.M., Gelade, G.: · 1980
Earlier work this paper cites.
Shifts in selective visual attention: Towards the underlying neural circuitry
Koch, C., Ullman, S.: · 1987
Earlier work this paper cites.
Shifter circuits: A computational strategy for dynamic aspects of visual processing
Anderson, C.H., Van Essen, D.C.: · 1987
Earlier work this paper cites.
Guided search 2.0 a revised model of visual search
Wolfe, J.M.: · 1994
Earlier work this paper cites.
Modeling visual attention via selective tuning
Tsotsos, J.K., Culhane, S.M., Wai, W.Y.K., Lai, Y., Davis, N., Nuflo, F.: · 1995
Earlier work this paper cites.
Neural mechanisms of selective visual attention
Desimone, R., Duncan, J.: · 1995
Earlier work this paper cites.
Visual attention mediated by biased competition in extrastriate visual cortex
Desimone, R.: · 1998
Earlier work this paper cites.
The normalization model of attention
Reynolds, J.H., Heeger, D.J.: · 2009
Earlier work this paper cites.
Top-down and bottom-up mechanisms in biasing competition in the human brain
Beck, D.M., Kastner, S.: · 2009
Earlier work this paper cites.
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2010
Earlier work this paper cites.
Mechanisms of top-down attention
Baluch, F., Itti, L.: · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
Cited alongside, same era.
Microsoft COCO: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
Cited alongside, same era.
Striving for simplicity: The all convolutional net
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: · 2014
Cited alongside, same era.
Recurrent convolutional neural networks for scene parsing
Pinheiro, P.H., Collobert, R.: · 2014
Cited alongside, same era.
Learning deep features for scene recognition using places database
Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: · 2015
Later among the works it cites.
Object detectors emerge in deep scene cnns
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: · 2015
Later among the works it cites.
Is object localization for free?-weakly-supervised learning with convolutional neural networks
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: · 2015
Later among the works it cites.
Constrained convolutional neural networks for weakly supervised segmentation
Pathak, D., Krahenbuhl, P., Darrell, T.: · 2015
Later among the works it cites.
Weakly-and semi-supervised learning of a dcnn for semantic image segmentation
Papandreou, G., Chen, L.C., Murphy, K., Yuille, A.L.: · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: · 2014
Cited alongside, same era.
In: ACM International Conference on Multimedia. (2014)
Caffe: Convolutional architecture for fast feature embedding · 2014
Cited alongside, same era.
Return of the devil in the details: Delving deep into convolutional nets
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014
Cited alongside, same era.
Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 2014
Cited alongside, same era.
Edge boxes: Locating object proposals from edges
Zitnick, C.L., Dollár, P.: · 2014
Cited alongside, same era.
Multiscale combinatorial grouping
Arbeláez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: · 2014
Cited alongside, same era.
Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks
Cao, C., Liu, X., Yang, Y., Yu, Y., Wang, J., Wang, Z., Huang, Y., Wang, L., Huang, C., Xu, W., et al.: · 2015
Cited alongside, same era.
Pinheiro, P.O., Collobert, R.: · 2015
Later among the works it cites.
From captions to visual concepts and back
Fang, H., Gupta, S., Iandola, F., Srivastava, R.K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J.C., et al.: · 2015
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
Later among the works it cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Later among the works it cites.
Understanding neural networks through deep visualization
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: · 2015
Later among the works it cites.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
Later among the works it cites.
Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: · 2016
Closest in time.
Fast and accurate deep network learning by exponential linear units (elus)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: · 2016
Closest in time.