Rakshith Shetty (PhD Student)

Publications

2021

Conference paper

F. Rezaeianaran, R. Shetty, R. Aljundi, D. O. Reino, S. Zhang, and B. Schiele

“Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection,” in ICCV 2021, IEEE/CVF International Conference on Computer Vision, Virtual Event, 2021.

@inproceedings{Rezaeianaran_ICCV21,
TITLE = {Seeking Similarities over Differences: {S}imilarity-based Domain Alignment for Adaptive Object Detection},
AUTHOR = {Rezaeianaran, Farzaneh and Shetty, Rakshith and Aljundi, Rahaf and Reino, Daniel Olmeda and Zhang, Shanshan and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-6654-2812-5},
DOI = {10.1109/ICCV48922.2021.00907},
PUBLISHER = {IEEE},
YEAR = {2021},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {ICCV 2021, IEEE/CVF International Conference on Computer Vision},
PAGES = {9184--9193},
ADDRESS = {Virtual Event},
}

Endnote

%0 Conference Proceedings
%A Rezaeianaran, Farzaneh
%A Shetty, Rakshith
%A Aljundi, Rahaf
%A Reino, Daniel Olmeda
%A Zhang, Shanshan
%A Schiele, Bernt
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0009-8116-C
%R 10.1109/ICCV48922.2021.00907
%D 2021
%B IEEE/CVF International Conference on Computer Vision
%Z date of event: 2021-10-11 - 2021-10-17
%C Virtual Event
%B ICCV 2021
%P 9184 - 9193
%I IEEE
%@  978-1-6654-2812-5

Thesis

D2IMPR-CS

R. Shetty

“Adversarial Content Manipulation for Analyzing and Improving Model Robustness,” Universität des Saarlandes, Saarbrücken, 2021.

@phdthesis{Shettyphd2020,
TITLE = {Adversarial Content Manipulation for Analyzing and Improving Model Robustness},
AUTHOR = {Shetty, Rakshith},
LANGUAGE = {eng},
URL = {nbn:de:bsz:291--ds-346515},
DOI = {10.22028/D291-34651},
SCHOOL = {Universit{\"a}t des Saarlandes},
ADDRESS = {Saarbr{\"u}cken},
YEAR = {2021},
MARGINALMARK = {$\bullet$},
DATE = {2021},
}

Endnote

%0 Thesis
%A Shetty, Rakshith
%Y Schiele, Bernt
%A referee: Fritz, Mario
%A referee: Torralba, Antonio
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
International Max Planck Research School, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Adversarial Content Manipulation for Analyzing and Improving 
Model Robustness : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0009-5D93-9
%R 10.22028/D291-34651 
%U nbn:de:bsz:291--ds-346515
%F OTHER: hdl:20.500.11880/31874
%I Universit&#228;t des Saarlandes
%C Saarbr&#252;cken
%D 2021
%P 191 p.
%V phd
%9 phd
%U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/31874

2020

Conference paper

R. Shetty, M. Fritz, and B. Schiele

“Towards Automated Testing and Robustification by Semantic Adversarial Data Generation,” in Computer Vision -- ECCV 2020, Glasgow, UK, 2020.

@inproceedings{Shetty_ECCV2020,
TITLE = {Towards Automated Testing and Robustification by Semantic Adversarial Data Generation},
AUTHOR = {Shetty, Rakshith and Fritz, Mario and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-3-030-58535-8},
DOI = {10.1007/978-3-030-58536-5_29},
PUBLISHER = {Springer},
YEAR = {2020},
DATE = {2020},
BOOKTITLE = {Computer Vision -- ECCV 2020},
EDITOR = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
PAGES = {489--506},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {12347},
ADDRESS = {Glasgow, UK},
}

Endnote

%0 Conference Proceedings
%A Shetty, Rakshith
%A Fritz, Mario
%A Schiele, Bernt
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Towards Automated Testing and Robustification by Semantic Adversarial Data Generation : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0007-6BA1-B
%R 10.1007/978-3-030-58536-5_29
%D 2020
%B 16th European Conference on Computer Vision
%Z date of event: 2020-08-23 - 2020-08-28
%C Glasgow, UK
%B Computer Vision -- ECCV 2020
%E Vedaldi, Andrea; Bischof, Horst; Brox, Thomas; Frahm, Jan-Michael
%P 489 - 506
%I Springer
%@ 978-3-030-58535-8
%B Lecture Notes in Computer Science
%N 12347

Conference paper

V. Agarwal, R. Shetty, and M. Fritz

“Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, USA (Virtual), 2020.

@inproceedings{agarwal2020towards,
TITLE = {Towards Causal {VQA}: {R}evealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing},
AUTHOR = {Agarwal, Vedika and Shetty, Rakshith and Fritz, Mario},
LANGUAGE = {eng},
ISBN = {978-1-7281-7168-5},
DOI = {10.1109/CVPR42600.2020.00971},
PUBLISHER = {IEEE},
YEAR = {2020},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)},
PAGES = {9687--9695},
ADDRESS = {Seattle, WA, USA (Virtual)},
}

Endnote

%0 Conference Proceedings
%A Agarwal, Vedika
%A Shetty, Rakshith
%A Fritz, Mario
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0005-7482-5
%R 10.1109/CVPR42600.2020.00971
%D 2020
%B 33rd IEEE Conference on Computer Vision and Pattern Recognition 
%Z date of event: 2020-06-14 - 2020-06-19
%C Seattle, WA, USA (Virtual)
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 9687 - 9695
%I IEEE
%@ 978-1-7281-7168-5

Conference paper

X. Hong, R. Shetty, A. Sayeed, K. Mehra, V. Demberg, and B. Schiele

“Diverse and Relevant Visual Storytelling with Scene Graph Embeddings,” in Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL 2020), Online, 2020.

@inproceedings{hong-etal-2020-diverse,
TITLE = {Diverse and Relevant Visual Storytelling with Scene Graph Embeddings},
AUTHOR = {Hong, Xudong and Shetty, Rakshith and Sayeed, Asad and Mehra, Khushboo and Demberg, Vera and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-952148-63-7},
DOI = {10.18653/v1/2020.conll-1.34},
PUBLISHER = {ACL},
YEAR = {2020},
BOOKTITLE = {Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL 2020)},
PAGES = {420--430},
ADDRESS = {Online},
}

Endnote

%0 Conference Proceedings
%A Hong, Xudong
%A Shetty, Rakshith
%A Sayeed, Asad
%A Mehra, Khushboo
%A Demberg, Vera
%A Schiele, Bernt
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Diverse and Relevant Visual Storytelling with Scene Graph Embeddings : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0007-AE1B-8
%R 10.18653/v1/2020.conll-1.34
%D 2020
%B 24th Conference on Computational Natural Language Learning
%Z date of event: 2020-11-19 - 2020-11-20
%C Online
%B Proceedings of the 24th Conference on Computational Natural Language Learning
%P 420 - 430
%I ACL
%@  978-1-952148-63-7
%U https://www.aclweb.org/anthology/2020.conll-1.34.pdf

2019

Conference paper

R. Shetty, B. Schiele, and M. Fritz

“Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, USA, 2019.

@inproceedings{shetty2018context,
TITLE = {Not Using the Car to See the Sidewalk: {Q}uantifying and Controlling the Effects of Context in Classification and Segmentation},
AUTHOR = {Shetty, Rakshith and Schiele, Bernt and Fritz, Mario},
LANGUAGE = {eng},
ISBN = {978-1-7281-3293-8},
DOI = {10.1109/CVPR.2019.00841},
PUBLISHER = {IEEE},
YEAR = {2019},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)},
PAGES = {8210--8218},
ADDRESS = {Long Beach, CA, USA},
}

Endnote

%0 Conference Proceedings
%A Shetty, Rakshith
%A Schiele, Bernt
%A Fritz, Mario
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Not Using the Car to See the Sidewalk: Quantifying and Controlling the
  Effects of Context in Classification and Segmentation : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0002-B487-A
%R 10.1109/CVPR.2019.00841
%D 2019
%B 32nd IEEE Conference on Computer Vision and Pattern Recognition
%Z date of event: 2019-06-16 - 2019-06-20
%C Long Beach, CA, USA
%B  IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 8210 - 8218
%I IEEE
%@ 978-1-7281-3293-8

2018

Conference paper

R. Shetty, M. Fritz, and B. Schiele

“Adversarial Scene Editing: Automatic Object Removal from Weak Supervision,” in Advances in Neural Information Processing Systems 31 (NeurIPS 2018), Montréal, Canada, 2018.

Abstract

While great progress has been made recently in automatic image manipulation,
it has been limited to object centric images like faces or structured scene
datasets. In this work, we take a step towards general scene-level image
editing by developing an automatic interaction-free object removal model. Our
model learns to find and remove objects from general scene images using
image-level labels and unpaired data in a generative adversarial network (GAN)
framework. We achieve this with two key contributions: a two-stage editor
architecture consisting of a mask generator and image in-painter that
co-operate to remove objects, and a novel GAN based prior for the mask
generator that allows us to flexibly incorporate knowledge about object shapes.
We experimentally show on two datasets that our method effectively removes a
wide variety of objects using weak supervision only

BibTeX

@inproceedings{NIPS2018_7997,
TITLE = {Adversarial Scene Editing: Automatic Object Removal from Weak Supervision},
AUTHOR = {Shetty, Rakshith and Fritz, Mario and Schiele, Bernt},
LANGUAGE = {eng},
PUBLISHER = {Curran Associates},
YEAR = {2018},
ABSTRACT = {While great progress has been made recently in automatic image manipulation,<br>it has been limited to object centric images like faces or structured scene<br>datasets. In this work, we take a step towards general scene-level image<br>editing by developing an automatic interaction-free object removal model. Our<br>model learns to find and remove objects from general scene images using<br>image-level labels and unpaired data in a generative adversarial network (GAN)<br>framework. We achieve this with two key contributions: a two-stage editor<br>architecture consisting of a mask generator and image in-painter that<br>co-operate to remove objects, and a novel GAN based prior for the mask<br>generator that allows us to flexibly incorporate knowledge about object shapes.<br>We experimentally show on two datasets that our method effectively removes a<br>wide variety of objects using weak supervision only<br>},
BOOKTITLE = {Advances in Neural Information Processing Systems 31 (NeurIPS 2018)},
EDITOR = {Bengio, S. and Wallach, H. and Larochelle, H. and Graumann, K. and Cesa-Bianchi, N. and Garnett, R.},
PAGES = {7716--7726},
ADDRESS = {Montr{\'e}al, Canada},
}

Endnote

%0 Conference Proceedings
%A Shetty, Rakshith
%A Fritz, Mario
%A Schiele, Bernt
%+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
%T Adversarial Scene Editing: Automatic Object Removal from Weak
  Supervision : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0001-7A92-1
%D 2018
%B Thirty-second Conference on Neural Information Processing Systems
%Z date of event: 2018-12-02 - 2018-12-08
%C Montr&#233;al, Canada
%X   While great progress has been made recently in automatic image manipulation,<br>it has been limited to object centric images like faces or structured scene<br>datasets. In this work, we take a step towards general scene-level image<br>editing by developing an automatic interaction-free object removal model. Our<br>model learns to find and remove objects from general scene images using<br>image-level labels and unpaired data in a generative adversarial network (GAN)<br>framework. We achieve this with two key contributions: a two-stage editor<br>architecture consisting of a mask generator and image in-painter that<br>co-operate to remove objects, and a novel GAN based prior for the mask<br>generator that allows us to flexibly incorporate knowledge about object shapes.<br>We experimentally show on two datasets that our method effectively removes a<br>wide variety of objects using weak supervision only<br>
%K Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Artificial Intelligence, cs.AI,Statistics, Machine Learning, stat.ML
%B Advances in Neural Information Processing Systems 31
%E Bengio, S.; Wallach, H.; Larochelle, H.; Graumann, K.; Cesa-Bianchi, N.; Garnett, R.
%P 7716 - 7726
%I Curran Associates
%U http://papers.nips.cc/paper/7997-adversarial-scene-editing-automatic-object-removal-from-weak-supervision.pdf

Conference paper

M. Wagner, H. Basevi, R. Shetty, W. Li, M. Malinowski, M. Fritz, and A. Leonardis

“Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions,” in Computer Vision - ECCV 2018 Workshops, Munich, Germany, 2019.

@inproceedings{wagner18eccvw,
TITLE = {Answering Visual What-If Questions: {F}rom Actions to Predicted Scene Descriptions},
AUTHOR = {Wagner, Misha and Basevi, Hector and Shetty, Rakshith and Li, Wenbin and Malinowski, Mateusz and Fritz, Mario and Leonardis, Ales},
LANGUAGE = {eng},
ISBN = {978-3-030-11008-6},
DOI = {10.1007/978-3-030-11009-3_32},
PUBLISHER = {Springer},
YEAR = {2018},
DATE = {2019},
BOOKTITLE = {Computer Vision -- ECCV 2018 Workshops},
EDITOR = {Leal-Taix{\'e}, Laura and Roth, Stefan},
PAGES = {521--537},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {11129},
ADDRESS = {Munich, Germany},
}

Endnote

%0 Conference Proceedings
%A Wagner, Misha
%A Basevi, Hector
%A Shetty, Rakshith
%A Li, Wenbin
%A Malinowski, Mateusz
%A Fritz, Mario
%A Leonardis, Ales
%+ External Organizations
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T Answering Visual What-If Questions: From Actions to Predicted Scene
  Descriptions : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0002-B962-F
%R 10.1007/978-3-030-11009-3_32
%D 2019
%B Workshop on Visual Learning and Embodied Agents in Simulation Environments
%Z date of event: 2018-09-09 - 2018-09-09
%C Munich, Germany
%B Computer Vision - ECCV 2018 Workshops
%E Leal-Taix&#233;, Laura; Roth, Stefan
%P 521 - 537
%I Springer
%@ 978-3-030-11008-6
%B Lecture Notes in Computer Science
%N 11129

Article

R. Shetty, H. R. Tavakoli, and J. Laaksonen

“Image and Video Captioning with Augmented Neural Architectures,” IEEE MultiMedia, vol. 25, no. 2, 2018.

@article{shetty2018image,
TITLE = {Image and Video Captioning with Augmented Neural Architectures},
AUTHOR = {Shetty, Rakshith and Tavakoli, Hamed Rezazadegan and Laaksonen, Jorma},
LANGUAGE = {eng},
ISSN = {1070-986X},
DOI = {10.1109/MMUL.2018.112135923},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2018},
DATE = {2018},
JOURNAL = {IEEE MultiMedia},
VOLUME = {25},
NUMBER = {2},
PAGES = {34--46},
}

Endnote

%0 Journal Article
%A Shetty, Rakshith
%A Tavakoli, Hamed Rezazadegan
%A Laaksonen, Jorma
%+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
%T Image and Video Captioning with Augmented Neural Architectures : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0001-7A95-E
%R 10.1109/MMUL.2018.112135923 
%7 2018
%D 2018
%J IEEE MultiMedia
%V 25
%N 2
%& 34
%P 34 - 46
%I IEEE
%C Piscataway, NJ
%@ false

Conference paper

R. Shetty, B. Schiele, and M. Fritz

“A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation,” in Proceedings of the 27th USENIX Security Symposium, Baltimore, MD, USA, 2018.

@inproceedings{shetty_USENIX2018,
TITLE = {$A^{4}NT$: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation},
AUTHOR = {Shetty, Rakshith and Schiele, Bernt and Fritz, Mario},
LANGUAGE = {eng},
ISBN = {978 -1- 931971- 4 6 -1},
PUBLISHER = {USENIX Association},
YEAR = {2018},
BOOKTITLE = {Proceedings of the 27th USENIX Security Symposium},
PAGES = {1633--1650},
ADDRESS = {Baltimore, MD, USA},
}

Endnote

%0 Conference Proceedings
%A Shetty, Rakshith
%A Schiele, Bernt
%A Fritz, Mario
%+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
%T A4NT: Author Attribute Anonymity by Adversarial Training of Neural
  Machine Translation : 
%G eng
%U http://hdl.handle.net/11858/00-001M-0000-002E-271D-B
%D 2018
%B 27th USENIX Security Symposium
%Z date of event: 2018-08-15 - 2018-08-17
%C Baltimore, MD, USA
%B Proceedings of the 27th USENIX Security Symposium
%P 1633 - 1650
%I USENIX Association
%@ 978 -1- 931971- 4 6 -1

2017

Conference paper

R. Shetty, M. Rohrbach, L. A. Hendricks, M. Fritz, and B. Schiele

“Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training,” in IEEE International Conference on Computer Vision (ICCV 2017), Venice, Italy, 2017.

@inproceedings{rakshith17iccv,
TITLE = {Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training},
AUTHOR = {Shetty, Rakshith and Rohrbach, Marcus and Hendricks, Lisa Anne and Fritz, Mario and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-5386-1032-9},
DOI = {10.1109/ICCV.2017.398},
PUBLISHER = {IEEE},
YEAR = {2017},
DATE = {2017},
BOOKTITLE = {IEEE International Conference on Computer Vision (ICCV 2017)},
PAGES = {4155--4164},
ADDRESS = {Venice, Italy},
}

Endnote

%0 Conference Proceedings
%A Shetty, Rakshith
%A Rohrbach, Marcus
%A Hendricks, Lisa Anne
%A Fritz, Mario
%A Schiele, Bernt
%+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
%T Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training : 
%G eng
%U http://hdl.handle.net/11858/00-001M-0000-002D-7CB3-3
%R 10.1109/ICCV.2017.398
%D 2017
%B International Conference on Computer Vision
%Z date of event: 2017-10-22 - 2017-10-29
%C Venice, Italy
%B IEEE International Conference on Computer Vision
%P 4155 - 4164
%I IEEE
%@ 978-1-5386-1032-9

Conference paper

H. R. Tavakoli, R. Shetty, A. Borji, and J. Laaksonen

“Paying Attention to Descriptions Generated by Image Captioning Models,” in IEEE International Conference on Computer Vision (ICCV 2017), Venice, Italy, 2017.

@inproceedings{tavakoliIccv2017,
TITLE = {Paying Attention to Descriptions Generated by Image Captioning Models},
AUTHOR = {Tavakoli, Hamed R. and Shetty, Rakshith and Borji, Ali and Laaksonen, Jorma},
LANGUAGE = {eng},
ISBN = {978-1-5386-1032-9},
DOI = {10.1109/ICCV.2017.272},
PUBLISHER = {IEEE},
YEAR = {2017},
DATE = {2017},
BOOKTITLE = {IEEE International Conference on Computer Vision (ICCV 2017)},
PAGES = {2506--2515},
ADDRESS = {Venice, Italy},
}

Endnote

%0 Conference Proceedings
%A Tavakoli, Hamed R.
%A Shetty, Rakshith
%A Borji, Ali
%A Laaksonen, Jorma
%+ External Organizations
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
%T Paying Attention to Descriptions Generated by Image Captioning Models : 
%G eng
%U http://hdl.handle.net/11858/00-001M-0000-002D-D199-1
%R 10.1109/ICCV.2017.272
%D 2017
%B International Conference on Computer Vision
%Z date of event: 2017-10-22 - 2017-10-29
%C Venice, Italy
%B IEEE International Conference on Computer Vision
%P 2506 - 2515
%I IEEE
%@ 978-1-5386-1032-9