Sorry, you need to enable JavaScript to visit this website.

Machine Learning for Signal Processing

Super-resolution of Omnidirectional Images Using Adversarial Learning


An omnidirectional image (ODI) enables viewers to look in every direction from a fixed point through a head-mounted display providing an immersive experience compared to that of a standard image. Designing immersive virtual reality systems with ODIs is challenging as they require high resolution content. In this paper, we study super-resolution for ODIs and propose an improved generative adversarial network based model which is optimized to handle the artifacts obtained in the spherical observational space.

Paper Details

Authors:
Aakanksha Rana, Aljosa Smolic
Submitted On:
30 September 2019 - 3:45am
Short Link:
Type:
Event:
Document Year:
Cite

Document Files

vsense_poster_template (3).pdf

(27)

Subscribe

[1] Aakanksha Rana, Aljosa Smolic, "Super-resolution of Omnidirectional Images Using Adversarial Learning ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4849. Accessed: Dec. 09, 2019.
@article{4849-19,
url = {http://sigport.org/4849},
author = {Aakanksha Rana; Aljosa Smolic },
publisher = {IEEE SigPort},
title = {Super-resolution of Omnidirectional Images Using Adversarial Learning },
year = {2019} }
TY - EJOUR
T1 - Super-resolution of Omnidirectional Images Using Adversarial Learning
AU - Aakanksha Rana; Aljosa Smolic
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4849
ER -
Aakanksha Rana, Aljosa Smolic. (2019). Super-resolution of Omnidirectional Images Using Adversarial Learning . IEEE SigPort. http://sigport.org/4849
Aakanksha Rana, Aljosa Smolic, 2019. Super-resolution of Omnidirectional Images Using Adversarial Learning . Available at: http://sigport.org/4849.
Aakanksha Rana, Aljosa Smolic. (2019). "Super-resolution of Omnidirectional Images Using Adversarial Learning ." Web.
1. Aakanksha Rana, Aljosa Smolic. Super-resolution of Omnidirectional Images Using Adversarial Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4849

Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning


The growing use of virtual autonomous agents in applications like games and entertainment demands better control policies for natural-looking movements and actions. Unlike the conventional approach of hard-coding motion routines, we propose a deep learning method for obtaining control policies by directly mimicking raw video demonstrations. Previous methods in this domain rely on extracting low-dimensional features from expert videos followed by a separate hand-crafted reward estimation step.

Paper Details

Authors:
Daiki Kimura, Asim Munawar, Ryuki Tachibana
Submitted On:
24 September 2019 - 4:46pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

mmps_final.pdf

(33)

Subscribe

[1] Daiki Kimura, Asim Munawar, Ryuki Tachibana, "Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4836. Accessed: Dec. 09, 2019.
@article{4836-19,
url = {http://sigport.org/4836},
author = {Daiki Kimura; Asim Munawar; Ryuki Tachibana },
publisher = {IEEE SigPort},
title = {Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning},
year = {2019} }
TY - EJOUR
T1 - Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning
AU - Daiki Kimura; Asim Munawar; Ryuki Tachibana
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4836
ER -
Daiki Kimura, Asim Munawar, Ryuki Tachibana. (2019). Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning. IEEE SigPort. http://sigport.org/4836
Daiki Kimura, Asim Munawar, Ryuki Tachibana, 2019. Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning. Available at: http://sigport.org/4836.
Daiki Kimura, Asim Munawar, Ryuki Tachibana. (2019). "Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning." Web.
1. Daiki Kimura, Asim Munawar, Ryuki Tachibana. Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4836

Single-image rain removal via multi-scale cascading image generation


A novel single-image rain removal method is proposed based on multi-scale cascading image generation (MSCG). In particular, the proposed method consists of an encoder extracting multi-scale features from images and a decoder generating de-rained images with a cascading mechanism. The encoder ensembles the convolution neural networks using the kernels with different sizes, and integrates their outputs across different scales.

Paper Details

Authors:
Zheng Zhang, Yi Xu, He Wang, Bingbing Ni, Hongteng Xu
Submitted On:
22 September 2019 - 2:38pm
Short Link:
Type:
Event:
Document Year:
Cite

Document Files

Poster ICIP 2019 Paper #2542.pdf

(16)

Subscribe

[1] Zheng Zhang, Yi Xu, He Wang, Bingbing Ni, Hongteng Xu, "Single-image rain removal via multi-scale cascading image generation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4817. Accessed: Dec. 09, 2019.
@article{4817-19,
url = {http://sigport.org/4817},
author = {Zheng Zhang; Yi Xu; He Wang; Bingbing Ni; Hongteng Xu },
publisher = {IEEE SigPort},
title = {Single-image rain removal via multi-scale cascading image generation},
year = {2019} }
TY - EJOUR
T1 - Single-image rain removal via multi-scale cascading image generation
AU - Zheng Zhang; Yi Xu; He Wang; Bingbing Ni; Hongteng Xu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4817
ER -
Zheng Zhang, Yi Xu, He Wang, Bingbing Ni, Hongteng Xu. (2019). Single-image rain removal via multi-scale cascading image generation. IEEE SigPort. http://sigport.org/4817
Zheng Zhang, Yi Xu, He Wang, Bingbing Ni, Hongteng Xu, 2019. Single-image rain removal via multi-scale cascading image generation. Available at: http://sigport.org/4817.
Zheng Zhang, Yi Xu, He Wang, Bingbing Ni, Hongteng Xu. (2019). "Single-image rain removal via multi-scale cascading image generation." Web.
1. Zheng Zhang, Yi Xu, He Wang, Bingbing Ni, Hongteng Xu. Single-image rain removal via multi-scale cascading image generation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4817

A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING


Convolutional neural networks (CNN) have shown state-of-the-art results for low-level computer vision problems such as stereo and monocular disparity estimations, but still, have much room to further improve their performance in terms of accuracy, numbers of parameters, etc. Recent works have uncovered the advantages of using an unsupervised scheme to train CNN’s to estimate monocular disparity, where only the relatively-easy-to-obtain stereo images are needed for training.

Paper Details

Authors:
Munchurl Kim
Submitted On:
19 September 2019 - 8:16am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:

Document Files

Poster_1748.pdf

(12)

Subscribe

[1] Munchurl Kim, "A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4712. Accessed: Dec. 09, 2019.
@article{4712-19,
url = {http://sigport.org/4712},
author = {Munchurl Kim },
publisher = {IEEE SigPort},
title = {A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING},
year = {2019} }
TY - EJOUR
T1 - A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING
AU - Munchurl Kim
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4712
ER -
Munchurl Kim. (2019). A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING. IEEE SigPort. http://sigport.org/4712
Munchurl Kim, 2019. A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING. Available at: http://sigport.org/4712.
Munchurl Kim. (2019). "A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING." Web.
1. Munchurl Kim. A NOVEL MONOCULAR DISPARITY ESTIMATION NETWORK WITH DOMAIN TRANSFORMATION AND AMBIGUITY LEARNING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4712

MULTI TASK LEARNING OF DEPTH FROM TELE AND WIDE STEREO IMAGE PAIRS

Paper Details

Authors:
Mostafa El-Khamy, Xianzhi Du, Haoyu Ren, Jungwon Lee
Submitted On:
19 September 2019 - 2:46am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Elkhamy_Telewide_depth_2019_ICIP.pdf

(34)

Subscribe

[1] Mostafa El-Khamy, Xianzhi Du, Haoyu Ren, Jungwon Lee, "MULTI TASK LEARNING OF DEPTH FROM TELE AND WIDE STEREO IMAGE PAIRS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4709. Accessed: Dec. 09, 2019.
@article{4709-19,
url = {http://sigport.org/4709},
author = {Mostafa El-Khamy; Xianzhi Du; Haoyu Ren; Jungwon Lee },
publisher = {IEEE SigPort},
title = {MULTI TASK LEARNING OF DEPTH FROM TELE AND WIDE STEREO IMAGE PAIRS},
year = {2019} }
TY - EJOUR
T1 - MULTI TASK LEARNING OF DEPTH FROM TELE AND WIDE STEREO IMAGE PAIRS
AU - Mostafa El-Khamy; Xianzhi Du; Haoyu Ren; Jungwon Lee
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4709
ER -
Mostafa El-Khamy, Xianzhi Du, Haoyu Ren, Jungwon Lee. (2019). MULTI TASK LEARNING OF DEPTH FROM TELE AND WIDE STEREO IMAGE PAIRS. IEEE SigPort. http://sigport.org/4709
Mostafa El-Khamy, Xianzhi Du, Haoyu Ren, Jungwon Lee, 2019. MULTI TASK LEARNING OF DEPTH FROM TELE AND WIDE STEREO IMAGE PAIRS. Available at: http://sigport.org/4709.
Mostafa El-Khamy, Xianzhi Du, Haoyu Ren, Jungwon Lee. (2019). "MULTI TASK LEARNING OF DEPTH FROM TELE AND WIDE STEREO IMAGE PAIRS." Web.
1. Mostafa El-Khamy, Xianzhi Du, Haoyu Ren, Jungwon Lee. MULTI TASK LEARNING OF DEPTH FROM TELE AND WIDE STEREO IMAGE PAIRS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4709

PORTRAIT SEGMENTATION BY DEEP REFINEMENT OF IMAGE MATTING


Portrait segmentation is becoming a hot topic nowadays.
In this paper we propose a novel framework to cope with
the high precision requirements that portrait segmentation
demands on boundary area by deep refinement of the
portrait matting. Our approach introduces three novel
techniques. First, a trimap is proposed by fusing information
coming from two well-known techniques for image
segmentation, i.e., Mask R-CNN and DensePose. Second,
an alpha matting algorithm runs over the previous trimap

Paper Details

Authors:
Carlos Orrite, Miguel Angel Varona, Eduardo Estopiñán, José Ramón Beltrán
Submitted On:
17 September 2019 - 6:00am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster paper code 2790

(29)

Keywords

Additional Categories

Subscribe

[1] Carlos Orrite, Miguel Angel Varona, Eduardo Estopiñán, José Ramón Beltrán, "PORTRAIT SEGMENTATION BY DEEP REFINEMENT OF IMAGE MATTING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4658. Accessed: Dec. 09, 2019.
@article{4658-19,
url = {http://sigport.org/4658},
author = {Carlos Orrite; Miguel Angel Varona; Eduardo Estopiñán; José Ramón Beltrán },
publisher = {IEEE SigPort},
title = {PORTRAIT SEGMENTATION BY DEEP REFINEMENT OF IMAGE MATTING},
year = {2019} }
TY - EJOUR
T1 - PORTRAIT SEGMENTATION BY DEEP REFINEMENT OF IMAGE MATTING
AU - Carlos Orrite; Miguel Angel Varona; Eduardo Estopiñán; José Ramón Beltrán
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4658
ER -
Carlos Orrite, Miguel Angel Varona, Eduardo Estopiñán, José Ramón Beltrán. (2019). PORTRAIT SEGMENTATION BY DEEP REFINEMENT OF IMAGE MATTING. IEEE SigPort. http://sigport.org/4658
Carlos Orrite, Miguel Angel Varona, Eduardo Estopiñán, José Ramón Beltrán, 2019. PORTRAIT SEGMENTATION BY DEEP REFINEMENT OF IMAGE MATTING. Available at: http://sigport.org/4658.
Carlos Orrite, Miguel Angel Varona, Eduardo Estopiñán, José Ramón Beltrán. (2019). "PORTRAIT SEGMENTATION BY DEEP REFINEMENT OF IMAGE MATTING." Web.
1. Carlos Orrite, Miguel Angel Varona, Eduardo Estopiñán, José Ramón Beltrán. PORTRAIT SEGMENTATION BY DEEP REFINEMENT OF IMAGE MATTING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4658

DISTRIBUTION PADDING IN CONVOLUTIONAL NEURAL NETWORKS


Even though zero padding is usually a staple in convolutional
neural networks to maintain the output size, it is highly suspicious
because it significantly alters the input distribution
around border region. To mitigate this problem, in this paper,
we propose a new padding technique termed as distribution
padding. The goal of the method is to approximately maintain
the statistics of the input border regions. We introduce
two different ways to achieve our goal. In both approaches,
the padded values are derived from the means of the border

Paper Details

Authors:
Anh-Duc Nguyen, Seonghwa Choi, Woojae Kim, Sewoong Ahn, Jinwoo Kim, Sanghoon Lee
Submitted On:
17 September 2019 - 3:06am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

distributional padding.pptx

(22)

Subscribe

[1] Anh-Duc Nguyen, Seonghwa Choi, Woojae Kim, Sewoong Ahn, Jinwoo Kim, Sanghoon Lee, "DISTRIBUTION PADDING IN CONVOLUTIONAL NEURAL NETWORKS ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4654. Accessed: Dec. 09, 2019.
@article{4654-19,
url = {http://sigport.org/4654},
author = {Anh-Duc Nguyen; Seonghwa Choi; Woojae Kim; Sewoong Ahn; Jinwoo Kim; Sanghoon Lee },
publisher = {IEEE SigPort},
title = {DISTRIBUTION PADDING IN CONVOLUTIONAL NEURAL NETWORKS },
year = {2019} }
TY - EJOUR
T1 - DISTRIBUTION PADDING IN CONVOLUTIONAL NEURAL NETWORKS
AU - Anh-Duc Nguyen; Seonghwa Choi; Woojae Kim; Sewoong Ahn; Jinwoo Kim; Sanghoon Lee
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4654
ER -
Anh-Duc Nguyen, Seonghwa Choi, Woojae Kim, Sewoong Ahn, Jinwoo Kim, Sanghoon Lee. (2019). DISTRIBUTION PADDING IN CONVOLUTIONAL NEURAL NETWORKS . IEEE SigPort. http://sigport.org/4654
Anh-Duc Nguyen, Seonghwa Choi, Woojae Kim, Sewoong Ahn, Jinwoo Kim, Sanghoon Lee, 2019. DISTRIBUTION PADDING IN CONVOLUTIONAL NEURAL NETWORKS . Available at: http://sigport.org/4654.
Anh-Duc Nguyen, Seonghwa Choi, Woojae Kim, Sewoong Ahn, Jinwoo Kim, Sanghoon Lee. (2019). "DISTRIBUTION PADDING IN CONVOLUTIONAL NEURAL NETWORKS ." Web.
1. Anh-Duc Nguyen, Seonghwa Choi, Woojae Kim, Sewoong Ahn, Jinwoo Kim, Sanghoon Lee. DISTRIBUTION PADDING IN CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4654

A History-based Stopping Criterion in Recursive Bayesian State Estimation


In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost.

Paper Details

Authors:
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus
Submitted On:
15 May 2019 - 9:57pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

P_Marghi_Kocan_ICASSP_2019.pdf

(45)

Subscribe

[1] Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus, "A History-based Stopping Criterion in Recursive Bayesian State Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4536. Accessed: Dec. 09, 2019.
@article{4536-19,
url = {http://sigport.org/4536},
author = {Yeganeh M. Marghi; Aziz Kocanaogullari; Murat Akcakaya; Deniz Erdomus },
publisher = {IEEE SigPort},
title = {A History-based Stopping Criterion in Recursive Bayesian State Estimation},
year = {2019} }
TY - EJOUR
T1 - A History-based Stopping Criterion in Recursive Bayesian State Estimation
AU - Yeganeh M. Marghi; Aziz Kocanaogullari; Murat Akcakaya; Deniz Erdomus
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4536
ER -
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. (2019). A History-based Stopping Criterion in Recursive Bayesian State Estimation. IEEE SigPort. http://sigport.org/4536
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus, 2019. A History-based Stopping Criterion in Recursive Bayesian State Estimation. Available at: http://sigport.org/4536.
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. (2019). "A History-based Stopping Criterion in Recursive Bayesian State Estimation." Web.
1. Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. A History-based Stopping Criterion in Recursive Bayesian State Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4536

A History-based Stopping Criterion in Recursive Bayesian State Estimation


In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost.

Paper Details

Authors:
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus
Submitted On:
15 May 2019 - 9:57pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

P_Marghi_Kocan_ICASSP_2019.pdf

(43)

Subscribe

[1] Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus, "A History-based Stopping Criterion in Recursive Bayesian State Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4535. Accessed: Dec. 09, 2019.
@article{4535-19,
url = {http://sigport.org/4535},
author = {Yeganeh M. Marghi; Aziz Kocanaogullari; Murat Akcakaya; Deniz Erdomus },
publisher = {IEEE SigPort},
title = {A History-based Stopping Criterion in Recursive Bayesian State Estimation},
year = {2019} }
TY - EJOUR
T1 - A History-based Stopping Criterion in Recursive Bayesian State Estimation
AU - Yeganeh M. Marghi; Aziz Kocanaogullari; Murat Akcakaya; Deniz Erdomus
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4535
ER -
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. (2019). A History-based Stopping Criterion in Recursive Bayesian State Estimation. IEEE SigPort. http://sigport.org/4535
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus, 2019. A History-based Stopping Criterion in Recursive Bayesian State Estimation. Available at: http://sigport.org/4535.
Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. (2019). "A History-based Stopping Criterion in Recursive Bayesian State Estimation." Web.
1. Yeganeh M. Marghi, Aziz Kocanaogullari, Murat Akcakaya, Deniz Erdomus. A History-based Stopping Criterion in Recursive Bayesian State Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4535

Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks

Paper Details

Authors:
Sumit Jha, Carlos Busso
Submitted On:
13 May 2019 - 9:22am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp-poster.pdf

(50)

Subscribe

[1] Sumit Jha, Carlos Busso, "Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4485. Accessed: Dec. 09, 2019.
@article{4485-19,
url = {http://sigport.org/4485},
author = {Sumit Jha; Carlos Busso },
publisher = {IEEE SigPort},
title = {Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks
AU - Sumit Jha; Carlos Busso
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4485
ER -
Sumit Jha, Carlos Busso. (2019). Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4485
Sumit Jha, Carlos Busso, 2019. Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks. Available at: http://sigport.org/4485.
Sumit Jha, Carlos Busso. (2019). "Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks." Web.
1. Sumit Jha, Carlos Busso. Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4485

Pages