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CNN Fixations: An unraveling approach to visualize the discriminative
  image regions
v1v2v3 (latest)

CNN Fixations: An unraveling approach to visualize the discriminative image regions

22 August 2017
Konda Reddy Mopuri
Utsav Garg
R. Venkatesh Babu
    AAML
ArXiv (abs)PDFHTML

Papers citing "CNN Fixations: An unraveling approach to visualize the discriminative image regions"

27 / 27 papers shown
Title
Fast Feature Fool: A data independent approach to universal adversarial
  perturbations
Fast Feature Fool: A data independent approach to universal adversarial perturbations
Konda Reddy Mopuri
Utsav Garg
R. Venkatesh Babu
AAML
89
206
0
18 Jul 2017
Visualizing Deep Neural Network Decisions: Prediction Difference
  Analysis
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
L. Zintgraf
Taco S. Cohen
T. Adel
Max Welling
FAtt
140
708
0
15 Feb 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
321
20,023
0
07 Oct 2016
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning
  Challenge
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
Oriol Vinyals
Alexander Toshev
Samy Bengio
D. Erhan
111
854
0
21 Sep 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN3DV
775
36,813
0
25 Aug 2016
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For
  Sketch Recognition
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
Ravi Kiran Sarvadevabhatla
Jogendra Nath Kundu
R. Venkatesh Babu
44
47
0
11 Aug 2016
Top-down Neural Attention by Excitation Backprop
Top-down Neural Attention by Excitation Backprop
Jianming Zhang
Zhe Lin
Jonathan Brandt
Xiaohui Shen
Stan Sclaroff
81
947
0
01 Aug 2016
A Powerful Generative Model Using Random Weights for the Deep Image
  Representation
A Powerful Generative Model Using Random Weights for the Deep Image Representation
Kun He
Yan Wang
John E. Hopcroft
110
77
0
15 Jun 2016
Training Region-based Object Detectors with Online Hard Example Mining
Training Region-based Object Detectors with Online Hard Example Mining
Abhinav Shrivastava
Abhinav Gupta
Ross B. Girshick
ObjD
151
2,418
0
12 Apr 2016
CRAFT Objects from Images
CRAFT Objects from Images
Bin Yang
Junjie Yan
Zhen Lei
Stan Z. Li
ObjD
67
123
0
12 Apr 2016
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed
  Systems
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi
Ashish Agarwal
P. Barham
E. Brevdo
Zhiwen Chen
...
Pete Warden
Martin Wattenberg
Martin Wicke
Yuan Yu
Xiaoqiang Zheng
276
11,151
0
14 Mar 2016
Learning Deep Features for Discriminative Localization
Learning Deep Features for Discriminative Localization
Bolei Zhou
A. Khosla
Àgata Lapedriza
A. Oliva
Antonio Torralba
SSLSSegFAtt
250
9,326
0
14 Dec 2015
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,020
0
10 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
151
4,897
0
14 Nov 2015
Learning to Segment Object Candidates
Learning to Segment Object Candidates
Pedro H. O. Pinheiro
R. Collobert
Piotr Dollar
SSeg
94
802
0
20 Jun 2015
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
  Networks
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren
Kaiming He
Ross B. Girshick
Jian Sun
AIMatObjD
520
62,294
0
04 Jun 2015
Striving for Simplicity: The All Convolutional Net
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
FAtt
248
4,672
0
21 Dec 2014
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
277
19,066
0
20 Dec 2014
Deep Visual-Semantic Alignments for Generating Image Descriptions
Deep Visual-Semantic Alignments for Generating Image Descriptions
A. Karpathy
Li Fei-Fei
134
5,585
0
07 Dec 2014
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
477
43,658
0
17 Sep 2014
Self-taught Object Localization with Deep Networks
Self-taught Object Localization with Deep Networks
Loris Bazzani
Alessandro Bergamo
Dragomir Anguelov
Lorenzo Torresani
SSLObjD
71
154
0
13 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,386
0
04 Sep 2014
ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLMObjD
1.7K
39,547
0
01 Sep 2014
Caffe: Convolutional Architecture for Fast Feature Embedding
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia
Evan Shelhamer
Jeff Donahue
Sergey Karayev
Jonathan Long
Ross B. Girshick
S. Guadarrama
Trevor Darrell
VLMBDL3DV
274
14,711
0
20 Jun 2014
Microsoft COCO: Common Objects in Context
Microsoft COCO: Common Objects in Context
Nayeon Lee
Michael Maire
Serge J. Belongie
Lubomir Bourdev
Ross B. Girshick
James Hays
Pietro Perona
Deva Ramanan
C. L. Zitnick
Piotr Dollár
ObjD
413
43,667
0
01 May 2014
Deep Inside Convolutional Networks: Visualising Image Classification
  Models and Saliency Maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
312
7,308
0
20 Dec 2013
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAttSSL
595
15,893
0
12 Nov 2013
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