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DEEP LEARNING
IN RETAIL &
ADVERTISING
SUMMIT
London, UK
16th March 2018
Xavier Giro-i-Nieto
xavier.giro@upc.edu
Associate Professor
Universitat Politecnica de Catalunya
Technical University of Catalonia
Learning Where and
When to Look
@DocXavi[Slides on GDrive]
#reworkRETAIL
A salient team
Kevin
McGuinness
Noel E.
O’Connor
Cristian
Canton
Junting
Pan
Marc
Assens
Marta
Coll
Elisa
Sayrol
Jordi
Torres
3
Click to learn more
4
Let’s play a
game!
The importance of visual attention
The importance of visual attention
The importance of visual attention
The importance of visual attention
The importance of visual attention
Why don’t we see the changes?
We don’t really see the whole image
We only focus on small specific regions: the salient parts
Human beings reliably attend to the same regions of images
when shown
What we perceive
Where we look
What we actually see
Can we predict WHEREhumans will look?
Yes! Computational models of visual saliency
Why might this be useful?
17Johanna Närväinen, Janne Laine, “Looking through their eyes” VTT Impulse 2016.
18Johanna Närväinen, Janne Laine, “Looking through their eyes” VTT Impulse 2016.
19Johanna Närväinen, Janne Laine, “Looking through their eyes” VTT Impulse 2016.
Image
(input)
Saliency
map
(ouput)
21John Markoff, “Scientists see promise in deep learning Programs”, The New York Times (2012).
Photo: Keith Penner
22Yazmin How, “Interview: Yoshua Bengio, Yann LeCun, Geoffrey Hinton” RE·WORK (2017).
23A Krizhevsky, I Sutskever, GE Hinton “Imagenet classification with deep convolutional neural networks” NIPS 2012
Orange
AlexNet
Photo: Keith Penner
24
DATA
AlexNet
ALGORITHMS
CNNs
COMPUTATION
25
JuntingNet
ALGORITHMS
CNNs
DATA COMPUTATION
26J. Pan, X. Giró-i-Nieto, “End-to-end Convolutional Network for Saliency Prediction” (CVPRW 2015)
End to end CNN for Saliency
Prediction
27
Upsample
+ filter
2D
map
J. Pan, X. Giró-i-Nieto, “End-to-end Convolutional Network for Saliency Prediction” (CVPRW 2015)
JuntingNet
28
29
JuntingNet
ALGORITHMS
CNNs
DATA COMPUTATION
30
SalNet
ALGORITHMS
CNNs
DATA COMPUTATION
31
Convolution 1 (7 × 7 × 96)
Input (size 320 × 240 × 3)
Local response normalization
Max pool (kernel 3 × 3 stride 2)
Convolution 2 (5 × 5 × 256)
Max pool (kernel 3 × 3 stride 2)
Convolution 3 (3 × 3 × 512)
Convolution 4 (5 × 5 × 512)
Convolution 5 (5 × 5 × 512)
Convolution 6 (7 × 7 × 256)
Convolution 7 (11 × 11 × 128)
Convolution 8 (13 × 13 × 1)
Deconvolution 1 (8 × 8 × 1, stride 4)
VGG-M
Pan, Junting, Kevin McGuinness, Elisa Sayrol, Xavier Giro-i-Nieto, and Noel E. O'Connor. "Shallow and
deep convolutional networks for saliency prediction." CVPR 2016.
SalNet
32
SalNet
ALGORITHMS
CNNs
DATA COMPUTATION
33
DATA COMPUTATION
ALGORITHMS
CNNs
ALGORITHMS
Adversarial
SalGAN
34
SalGAN
DATA COMPUTATION
ALGORITHMS
CNNs
ALGORITHMS
Adversarial
35Credit: Santiago Pascual [slides] [video]
36
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron
Courville, and Yoshua Bengio. "Generative adversarial nets." NIPS 2014
Interview to Ian Goodfellow at Re-Work Deep Learning Summit San Francisco, 2018.
Adversarial Training
37
Junting Pan, Cristian Canton, Kevin McGuinness, Noel E. O’Connor, Jordi Torres, Elisa Sayrol and Xavier
Giro-i-Nieto. “SalGAN: Visual Saliency Prediction with Generative Adversarial Networks.” CVPRW 2017.
Generator
SalGAN
Discriminator
Can we predict WHERE & WHEN
humans will look?
Challenge:
Create a model able to predict visual scanpaths
40
SalTiNet
ALGORITHMS
CNNs
DATA
COMPUTATION
41
SalTiNet
ALGORITHMS
CNNs
DATA
COMPUTATION
42
Yashas Rai, Jesús Gutiérrez, and Patrick Le Callet. 2017. “A Dataset of Head and Eye
Movements for 360 Degree Images“. MMSys 2017
43
Yashas Rai, Jesús Gutiérrez, and Patrick Le Callet. 2017. “A Dataset of Head and Eye
Movements for 360 Degree Images“. MMSys 2017
44
SalTiNet
ALGORITHMS
CNNs
DATA
COMPUTATION
45
Create a model able to predict visual scanpaths
SalTiNet solution:
Add a TIME dimension to saliency maps with saliency volumes
and sample from them.
Challenge:
46
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “SaltiNet: Scan-Path
Prediction on 360 Degree Images Using Saliency Volumes.” ICCVW 2017.
width
height
timeSaliency Volume
47
00000000000000000
00000000000000000
00000000000000000
00000000000000000
00000000000000000
00000000000000000
00000000000000000 width
height
time
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “SaltiNet: Scan-Path
Prediction on 360 Degree Images Using Saliency Volumes.” ICCVW 2017.
Saliency Volume
48
00000000000000000
00000000000000000
00010000000010000
00000000000000000
00000000000000000
00000000010000000
00000000000000000
Fixation points : (x, y, t)
width
height
time
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “SaltiNet: Scan-Path
Prediction on 360 Degree Images Using Saliency Volumes.” ICCVW 2017.
Saliency Volume
49
00000000000000000
00000000000000000
00010000000010000
00000000000000000
00000000000000000
00000000010000000
00000000000000000
Convolution
Fixation points : (x, y, t)
width
height
timeSaliency Volume
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “SaltiNet: Scan-Path
Prediction on 360 Degree Images Using Saliency Volumes.” ICCVW 2017.
50
Predicted saliency volume
SalTiNet
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “SaltiNet: Scan-Path
Prediction on 360 Degree Images Using Saliency Volumes.” ICCVW 2017.
51
Stochastic sampling
SalTiNet
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “SaltiNet: Scan-Path
Prediction on 360 Degree Images Using Saliency Volumes.” ICCVW 2017.
52
Winners of IEEE ICME
2017 Challenge
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “SaltiNet: Scan-Path
Prediction on 360 Degree Images Using Saliency Volumes.” ICCVW 2017.
53
SalTiNet
ALGORITHMS
CNNs
DATA
COMPUTATION
54
PathGAN
DATA
COMPUTATION
ALGORITHMS
CNN & RNN
ALGORITHMS
Adversarial
55
PathGAN
DATA
COMPUTATION
ALGORITHMS
CNN & RNN
ALGORITHMS
Adversarial
56
Create a model able to predict visual scanpaths
PathGAN solution:
Generate a sequence of fixation points with recurrent neural
networks (RNNs).
Challenge:
57
Recurrent Neural Network (RNN)
Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9, no. 8
(1997): 1735-1780.
58
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “PathGan: Visual Scan-path
Prediction with Generative Adversarial Networks“ arXiv 2018.
PathGAN
59
Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto and Noel E. O’Connor. “PathGan: Visual Scan-path
Prediction with Generative Adversarial Networks“ arXiv 2018.
PathGAN
60
PathGAN
DATA
COMPUTATION
ALGORITHMS
CNN & RNN
ALGORITHMS
Adversarial
SalNetJuntingNet SalGAN
SaltiNet PathGAN
Saliency maps
Scanpaths
2016 20172015
Convolutional Neural Networks (CNNs)
Recurrent
Networks
Transfer Learning from ImageNet
Adversarial Training
2018
Scanpath Prediction
● MSc course (2017)
● BSc course (2018)
63
Deep Learning courses UPC Barcelona
● 1st edition (2016)
● 2nd edition (2017)
● 3rd edition (2018)
● 1st edition (2017)
● 2nd edition (2018)
Next edition Autumn 2018 Next edition Winter/Spring 2019Summer School (late June 2018)
@DocXavi
Xavier Giro-i-Nieto
Slides available at:
http://bit.ly/reworkRETAIL
We are looking for both
industrial & academic partners:
xavier.giro@upc.edu
#reworkRETAIL

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