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Predicting and Explaining Mobile UI Tappability with Vision Modeling and
  Saliency Analysis

Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis

5 April 2022
E. Schoop
Xin Zhou
Gang Li
Zhourong Chen
Björn Hartmann
Yang Li
    HAI
    FAtt
ArXivPDFHTML

Papers citing "Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis"

23 / 23 papers shown
Title
Screen Parsing: Towards Reverse Engineering of UI Models from
  Screenshots
Screen Parsing: Towards Reverse Engineering of UI Models from Screenshots
Jason Wu
Xiaoyi Zhang
Jeffrey Nichols
Jeffrey P. Bigham
3DV
178
71
0
17 Sep 2021
UIBert: Learning Generic Multimodal Representations for UI Understanding
UIBert: Learning Generic Multimodal Representations for UI Understanding
Chongyang Bai
Xiaoxue Zang
Ying Xu
Srinivas Sunkara
Abhinav Rastogi
Jindong Chen
Blaise Agüera y Arcas
58
94
0
29 Jul 2021
Multimodal Icon Annotation For Mobile Applications
Multimodal Icon Annotation For Mobile Applications
Xiaoxue Zang
Ying Xu
Jindong Chen
48
19
0
09 Jul 2021
Best of both worlds: local and global explanations with
  human-understandable concepts
Best of both worlds: local and global explanations with human-understandable concepts
Jessica Schrouff
Sebastien Baur
Shaobo Hou
Diana Mincu
Eric Loreaux
Ralph Blanes
James Wexler
Alan Karthikesalingam
Been Kim
FAtt
69
28
0
16 Jun 2021
Screen Recognition: Creating Accessibility Metadata for Mobile
  Applications from Pixels
Screen Recognition: Creating Accessibility Metadata for Mobile Applications from Pixels
Xiaoyi Zhang
Lilian de Greef
Amanda Swearngin
Samuel White
Kyle I. Murray
...
Jeffrey Nichols
Jason Wu
Chris Fleizach
Aaron Everitt
Jeffrey P. Bigham
306
170
0
13 Jan 2021
Screen2Vec: Semantic Embedding of GUI Screens and GUI Components
Screen2Vec: Semantic Embedding of GUI Screens and GUI Components
Toby Jia-Jun Li
Lindsay Popowski
Tom Michael Mitchell
Brad A. Myers
53
104
0
11 Jan 2021
ActionBert: Leveraging User Actions for Semantic Understanding of User
  Interfaces
ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces
Zecheng He
Srinivas Sunkara
Xiaoxue Zang
Ying Xu
Lijuan Liu
Nevan Wichers
Gabriel Schubiner
Ruby B. Lee
Jindong Chen
Blaise Agüera y Arcas
61
79
0
22 Dec 2020
Predicting Visual Importance Across Graphic Design Types
Predicting Visual Importance Across Graphic Design Types
Camilo Luciano Fosco
Vincent Casser
A. K. Bedi
Peter O'Donovan
Aaron Hertzmann
Zoya Bylinskii
HAI
42
58
0
07 Aug 2020
GUIComp: A GUI Design Assistant with Real-Time, Multi-Faceted Feedback
GUIComp: A GUI Design Assistant with Real-Time, Multi-Faceted Feedback
Chunggi Lee
Sanghoon Kim
Dongyun Han
Hongjun Yang
Youngwoo Park
Bum Chul Kwon
Sungahn Ko
36
61
0
16 Jan 2020
Scout: Rapid Exploration of Interface Layout Alternatives through
  High-Level Design Constraints
Scout: Rapid Exploration of Interface Layout Alternatives through High-Level Design Constraints
Amanda Swearngin
Chenglong Wang
A. Oleson
J. Fogarty
Amy J. Ko
32
58
0
15 Jan 2020
XRAI: Better Attributions Through Regions
XRAI: Better Attributions Through Regions
A. Kapishnikov
Tolga Bolukbasi
Fernanda Viégas
Michael Terry
FAtt
XAI
55
212
0
06 Jun 2019
Modeling Mobile Interface Tappability Using Crowdsourcing and Deep
  Learning
Modeling Mobile Interface Tappability Using Crowdsourcing and Deep Learning
Amanda Swearngin
Yongqian Li
HAI
41
62
0
28 Feb 2019
ImageNet-trained CNNs are biased towards texture; increasing shape bias
  improves accuracy and robustness
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos
Patricia Rubisch
Claudio Michaelis
Matthias Bethge
Felix Wichmann
Wieland Brendel
96
2,662
0
29 Nov 2018
Sanity Checks for Saliency Maps
Sanity Checks for Saliency Maps
Julius Adebayo
Justin Gilmer
M. Muelly
Ian Goodfellow
Moritz Hardt
Been Kim
FAtt
AAML
XAI
123
1,963
0
08 Oct 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust
  Deep Learning
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
Nicolas Papernot
Patrick McDaniel
OOD
AAML
141
507
0
13 Mar 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with
  Concept Activation Vectors (TCAV)
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim
Martin Wattenberg
Justin Gilmer
Carrie J. Cai
James Wexler
F. Viégas
Rory Sayres
FAtt
203
1,837
0
30 Nov 2017
Learning Visual Importance for Graphic Designs and Data Visualizations
Learning Visual Importance for Graphic Designs and Data Visualizations
Zoya Bylinskii
N. Kim
Peter O'Donovan
Sami Alsheikh
Spandan Madan
Hanspeter Pfister
F. Durand
Bryan C. Russell
Aaron Hertzmann
HAI
51
165
0
08 Aug 2017
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.0K
21,815
0
22 May 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
175
5,968
0
04 Mar 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
264
19,929
0
07 Oct 2016
The Mythos of Model Interpretability
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
168
3,685
0
10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
1.1K
16,931
0
16 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.1K
193,426
0
10 Dec 2015
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