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Machine Learning for Signal Processing

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
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[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: Oct. 14, 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
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[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: Oct. 14, 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
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[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: Oct. 14, 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
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P_Marghi_Kocan_ICASSP_2019.pdf

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[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: Oct. 14, 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
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[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: Oct. 14, 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

Adversarial Speaker Adaptation


We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model.

Paper Details

Authors:
Zhong Meng, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:26pm
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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Adversarial Speaker Adaptation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4475. Accessed: Oct. 14, 2019.
@article{4475-19,
url = {http://sigport.org/4475},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Adaptation},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Adaptation
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4475
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Adaptation. IEEE SigPort. http://sigport.org/4475
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Adaptation. Available at: http://sigport.org/4475.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Adaptation." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Adversarial Speaker Adaptation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4475

Attentive Adversarial Learning for Domain-Invariant Training


Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function.

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Authors:
Zhong Meng, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:03pm
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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Attentive Adversarial Learning for Domain-Invariant Training", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4474. Accessed: Oct. 14, 2019.
@article{4474-19,
url = {http://sigport.org/4474},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Attentive Adversarial Learning for Domain-Invariant Training},
year = {2019} }
TY - EJOUR
T1 - Attentive Adversarial Learning for Domain-Invariant Training
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4474
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Attentive Adversarial Learning for Domain-Invariant Training. IEEE SigPort. http://sigport.org/4474
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Attentive Adversarial Learning for Domain-Invariant Training. Available at: http://sigport.org/4474.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Attentive Adversarial Learning for Domain-Invariant Training." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Attentive Adversarial Learning for Domain-Invariant Training [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4474

Adversarial Speaker Verification


The use of deep networks to extract embeddings for speaker recognition has proven successfully. However, such embeddings are susceptible to performance degradation due to the mismatches among the training, enrollment, and test conditions. In this work, we propose an adversarial speaker verification (ASV) scheme to learn the condition-invariant deep embedding via adversarial multi-task training. In ASV, a speaker classification network and a condition identification network are jointly optimized to minimize the speaker classification loss and simultaneously mini-maximize the condition loss.

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Authors:
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:24pm
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[1] Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, "Adversarial Speaker Verification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4473. Accessed: Oct. 14, 2019.
@article{4473-19,
url = {http://sigport.org/4473},
author = {Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Verification},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Verification
AU - Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4473
ER -
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Verification. IEEE SigPort. http://sigport.org/4473
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Verification. Available at: http://sigport.org/4473.
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Verification." Web.
1. Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. Adversarial Speaker Verification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4473

Conditional Teacher-Student Learning


The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance.

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Authors:
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong
Submitted On:
12 May 2019 - 9:23pm
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[1] Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, "Conditional Teacher-Student Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4472. Accessed: Oct. 14, 2019.
@article{4472-19,
url = {http://sigport.org/4472},
author = {Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong },
publisher = {IEEE SigPort},
title = {Conditional Teacher-Student Learning},
year = {2019} }
TY - EJOUR
T1 - Conditional Teacher-Student Learning
AU - Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4472
ER -
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). Conditional Teacher-Student Learning. IEEE SigPort. http://sigport.org/4472
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, 2019. Conditional Teacher-Student Learning. Available at: http://sigport.org/4472.
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). "Conditional Teacher-Student Learning." Web.
1. Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. Conditional Teacher-Student Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4472

Deep Signal Recovery With One-Bit Quantization

Paper Details

Authors:
Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar
Submitted On:
10 May 2019 - 6:46pm
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[1] Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar, "Deep Signal Recovery With One-Bit Quantization", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4422. Accessed: Oct. 14, 2019.
@article{4422-19,
url = {http://sigport.org/4422},
author = {Shahin Khobahi; Naveed Naimipour; Mojtaba Soltanalian; Yonina C. Eldar },
publisher = {IEEE SigPort},
title = {Deep Signal Recovery With One-Bit Quantization},
year = {2019} }
TY - EJOUR
T1 - Deep Signal Recovery With One-Bit Quantization
AU - Shahin Khobahi; Naveed Naimipour; Mojtaba Soltanalian; Yonina C. Eldar
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4422
ER -
Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar. (2019). Deep Signal Recovery With One-Bit Quantization. IEEE SigPort. http://sigport.org/4422
Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar, 2019. Deep Signal Recovery With One-Bit Quantization. Available at: http://sigport.org/4422.
Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar. (2019). "Deep Signal Recovery With One-Bit Quantization." Web.
1. Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar. Deep Signal Recovery With One-Bit Quantization [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4422

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