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Pattern recognition and classification (MLR-PATT)

WITCHcraft: Efficient PGD Attacks with Random Step Size

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Authors:
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi
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18 May 2020 - 6:05pm
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WITCHcraft ICASSP 2020.pdf

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[1] Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi, "WITCHcraft: Efficient PGD Attacks with Random Step Size", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5396. Accessed: Oct. 26, 2020.
@article{5396-20,
url = {http://sigport.org/5396},
author = {Ping-Yeh Chiang; Jonas Geiping; Micah Goldblum; Tom Goldstein; Renkun Ni; Steven Reich; Ali Shafahi },
publisher = {IEEE SigPort},
title = {WITCHcraft: Efficient PGD Attacks with Random Step Size},
year = {2020} }
TY - EJOUR
T1 - WITCHcraft: Efficient PGD Attacks with Random Step Size
AU - Ping-Yeh Chiang; Jonas Geiping; Micah Goldblum; Tom Goldstein; Renkun Ni; Steven Reich; Ali Shafahi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5396
ER -
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. (2020). WITCHcraft: Efficient PGD Attacks with Random Step Size. IEEE SigPort. http://sigport.org/5396
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi, 2020. WITCHcraft: Efficient PGD Attacks with Random Step Size. Available at: http://sigport.org/5396.
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. (2020). "WITCHcraft: Efficient PGD Attacks with Random Step Size." Web.
1. Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. WITCHcraft: Efficient PGD Attacks with Random Step Size [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5396

Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)


State-of-the-art hearing aids (HA) are limited in recognizing acoustic environments. Much effort is spent on research to improve listening experience for HA users in every acoustic situation. There is, however, no dedicated public database to train acoustic environment recognition algorithms with a specific focus on HA applications accounting for their requirements. Existing acoustic scene classification databases are inappropriate for HA signal processing.

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Authors:
Kamil Adiloğlu, Jörg-Hendrik Bach
Submitted On:
18 May 2020 - 7:01am
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https://download.hoertech.de/hear-ds-data/HEAR-DS/RawAudioCuts/doc/icassp2020-hear-ds-presentation-huewel.mp4

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[1] Kamil Adiloğlu, Jörg-Hendrik Bach, "Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5392. Accessed: Oct. 26, 2020.
@article{5392-20,
url = {http://sigport.org/5392},
author = {Kamil Adiloğlu; Jörg-Hendrik Bach },
publisher = {IEEE SigPort},
title = {Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)},
year = {2020} }
TY - EJOUR
T1 - Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)
AU - Kamil Adiloğlu; Jörg-Hendrik Bach
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5392
ER -
Kamil Adiloğlu, Jörg-Hendrik Bach. (2020). Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS). IEEE SigPort. http://sigport.org/5392
Kamil Adiloğlu, Jörg-Hendrik Bach, 2020. Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS). Available at: http://sigport.org/5392.
Kamil Adiloğlu, Jörg-Hendrik Bach. (2020). "Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS)." Web.
1. Kamil Adiloğlu, Jörg-Hendrik Bach. Hearing Aid Research Data Set for Acoustic Environment Recognition (HEAR-DS) [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5392

Embedded Large–Scale Handwritten Chinese Character Recognition


As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time across a range of mobile devices.

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Authors:
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda
Submitted On:
16 May 2020 - 11:46am
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_Embedded Large Scale Handwritten Chinese Character.pdf

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[1] Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda, "Embedded Large–Scale Handwritten Chinese Character Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5377. Accessed: Oct. 26, 2020.
@article{5377-20,
url = {http://sigport.org/5377},
author = {Hans J. G. A. Dolfing; Ryan S. Dixon; Jerome R. Bellegarda },
publisher = {IEEE SigPort},
title = {Embedded Large–Scale Handwritten Chinese Character Recognition},
year = {2020} }
TY - EJOUR
T1 - Embedded Large–Scale Handwritten Chinese Character Recognition
AU - Hans J. G. A. Dolfing; Ryan S. Dixon; Jerome R. Bellegarda
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5377
ER -
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. (2020). Embedded Large–Scale Handwritten Chinese Character Recognition. IEEE SigPort. http://sigport.org/5377
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda, 2020. Embedded Large–Scale Handwritten Chinese Character Recognition. Available at: http://sigport.org/5377.
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. (2020). "Embedded Large–Scale Handwritten Chinese Character Recognition." Web.
1. Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. Embedded Large–Scale Handwritten Chinese Character Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5377

GRAPH METRIC LEARNING VIA GERSHGORIN DISC ALIGNMENT


We propose a general projection-free metric learning framework, where the minimization objective $\min_{\M \in \cS} Q(\M)$ is a convex differentiable function of the metric matrix $\M$, and $\M$ resides in the set $\cS$ of generalized graph Laplacian matrices for connected graphs with positive edge weights and node degrees.
Unlike low-rank metric matrices common in the literature, $\cS$ includes the important positive-diagonal-only matrices as a special case in the limit.

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Authors:
Cheng Yang, Gene Cheung, Wei Hu
Submitted On:
15 May 2020 - 4:01pm
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presentation slides

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[1] Cheng Yang, Gene Cheung, Wei Hu, "GRAPH METRIC LEARNING VIA GERSHGORIN DISC ALIGNMENT", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5352. Accessed: Oct. 26, 2020.
@article{5352-20,
url = {http://sigport.org/5352},
author = {Cheng Yang; Gene Cheung; Wei Hu },
publisher = {IEEE SigPort},
title = {GRAPH METRIC LEARNING VIA GERSHGORIN DISC ALIGNMENT},
year = {2020} }
TY - EJOUR
T1 - GRAPH METRIC LEARNING VIA GERSHGORIN DISC ALIGNMENT
AU - Cheng Yang; Gene Cheung; Wei Hu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5352
ER -
Cheng Yang, Gene Cheung, Wei Hu. (2020). GRAPH METRIC LEARNING VIA GERSHGORIN DISC ALIGNMENT. IEEE SigPort. http://sigport.org/5352
Cheng Yang, Gene Cheung, Wei Hu, 2020. GRAPH METRIC LEARNING VIA GERSHGORIN DISC ALIGNMENT. Available at: http://sigport.org/5352.
Cheng Yang, Gene Cheung, Wei Hu. (2020). "GRAPH METRIC LEARNING VIA GERSHGORIN DISC ALIGNMENT." Web.
1. Cheng Yang, Gene Cheung, Wei Hu. GRAPH METRIC LEARNING VIA GERSHGORIN DISC ALIGNMENT [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5352

A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition


Epilepsy affects approximately 1% of the world’s population. Semiology of epileptic seizures contain major clinical signs to classify epilepsy syndromes currently evaluated by epileptologists by simple visual inspection of video. There is a necessity to create automatic and semiautomatic methods for seizure detection and classification to better support patient monitoring management and diagnostic decisions. One of the current promising approaches are the marker-less computer-vision techniques.

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Authors:
Tamás Karácsony ; Anna Mira Loesch-Biffar ; Christian Vollmar ; Soheyl Noachtar ; João Paulo Silva Cunha
Submitted On:
14 May 2020 - 8:20am
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ICASSP2020 slides

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[1] Tamás Karácsony ; Anna Mira Loesch-Biffar ; Christian Vollmar ; Soheyl Noachtar ; João Paulo Silva Cunha, "A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5295. Accessed: Oct. 26, 2020.
@article{5295-20,
url = {http://sigport.org/5295},
author = {Tamás Karácsony ; Anna Mira Loesch-Biffar ; Christian Vollmar ; Soheyl Noachtar ; João Paulo Silva Cunha },
publisher = {IEEE SigPort},
title = {A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition},
year = {2020} }
TY - EJOUR
T1 - A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition
AU - Tamás Karácsony ; Anna Mira Loesch-Biffar ; Christian Vollmar ; Soheyl Noachtar ; João Paulo Silva Cunha
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5295
ER -
Tamás Karácsony ; Anna Mira Loesch-Biffar ; Christian Vollmar ; Soheyl Noachtar ; João Paulo Silva Cunha. (2020). A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition. IEEE SigPort. http://sigport.org/5295
Tamás Karácsony ; Anna Mira Loesch-Biffar ; Christian Vollmar ; Soheyl Noachtar ; João Paulo Silva Cunha, 2020. A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition. Available at: http://sigport.org/5295.
Tamás Karácsony ; Anna Mira Loesch-Biffar ; Christian Vollmar ; Soheyl Noachtar ; João Paulo Silva Cunha. (2020). "A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition." Web.
1. Tamás Karácsony ; Anna Mira Loesch-Biffar ; Christian Vollmar ; Soheyl Noachtar ; João Paulo Silva Cunha. A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5295

Efficient Scene Text Detection with Textual Attention Tower


Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multi-oriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections.

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Authors:
Liang Zhang, Yufei Liu, Hang Xiao, Guangming zhu, Syed Afaq Shah, Mohammed Bennamoun, Peiyi Shen
Submitted On:
14 May 2020 - 7:47am
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icassp_presentation.pdf

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[1] Liang Zhang, Yufei Liu, Hang Xiao, Guangming zhu, Syed Afaq Shah, Mohammed Bennamoun, Peiyi Shen, "Efficient Scene Text Detection with Textual Attention Tower", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5289. Accessed: Oct. 26, 2020.
@article{5289-20,
url = {http://sigport.org/5289},
author = {Liang Zhang; Yufei Liu; Hang Xiao; Guangming zhu; Syed Afaq Shah; Mohammed Bennamoun; Peiyi Shen },
publisher = {IEEE SigPort},
title = {Efficient Scene Text Detection with Textual Attention Tower},
year = {2020} }
TY - EJOUR
T1 - Efficient Scene Text Detection with Textual Attention Tower
AU - Liang Zhang; Yufei Liu; Hang Xiao; Guangming zhu; Syed Afaq Shah; Mohammed Bennamoun; Peiyi Shen
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5289
ER -
Liang Zhang, Yufei Liu, Hang Xiao, Guangming zhu, Syed Afaq Shah, Mohammed Bennamoun, Peiyi Shen. (2020). Efficient Scene Text Detection with Textual Attention Tower. IEEE SigPort. http://sigport.org/5289
Liang Zhang, Yufei Liu, Hang Xiao, Guangming zhu, Syed Afaq Shah, Mohammed Bennamoun, Peiyi Shen, 2020. Efficient Scene Text Detection with Textual Attention Tower. Available at: http://sigport.org/5289.
Liang Zhang, Yufei Liu, Hang Xiao, Guangming zhu, Syed Afaq Shah, Mohammed Bennamoun, Peiyi Shen. (2020). "Efficient Scene Text Detection with Textual Attention Tower." Web.
1. Liang Zhang, Yufei Liu, Hang Xiao, Guangming zhu, Syed Afaq Shah, Mohammed Bennamoun, Peiyi Shen. Efficient Scene Text Detection with Textual Attention Tower [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5289

Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images

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Authors:
Claude Cariou, Kacem Chehdi , Steven Le Moan
Submitted On:
14 May 2020 - 2:05am
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3697.pdf

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[1] Claude Cariou, Kacem Chehdi , Steven Le Moan , "Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5232. Accessed: Oct. 26, 2020.
@article{5232-20,
url = {http://sigport.org/5232},
author = {Claude Cariou; Kacem Chehdi ; Steven Le Moan },
publisher = {IEEE SigPort},
title = {Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images},
year = {2020} }
TY - EJOUR
T1 - Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images
AU - Claude Cariou; Kacem Chehdi ; Steven Le Moan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5232
ER -
Claude Cariou, Kacem Chehdi , Steven Le Moan . (2020). Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images. IEEE SigPort. http://sigport.org/5232
Claude Cariou, Kacem Chehdi , Steven Le Moan , 2020. Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images. Available at: http://sigport.org/5232.
Claude Cariou, Kacem Chehdi , Steven Le Moan . (2020). "Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images." Web.
1. Claude Cariou, Kacem Chehdi , Steven Le Moan . Improved Nearest Neighbor Density-Based Clustering Techniques with Application to Hyperspectral Images [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5232

An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers


As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previously-proposed end-to-end detection frameworks have shown to be effective in spotting attacks for one particular spoofing strategy, they have relied on different models, architectures, and speech representations, depending on the spoofing strategy.

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Authors:
Jahangir Alam,Tiago Falk
Submitted On:
13 May 2020 - 5:21pm
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ICASSP_Spoofing.pdf

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[1] Jahangir Alam,Tiago Falk, "An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5149. Accessed: Oct. 26, 2020.
@article{5149-20,
url = {http://sigport.org/5149},
author = {Jahangir Alam;Tiago Falk },
publisher = {IEEE SigPort},
title = {An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers},
year = {2020} }
TY - EJOUR
T1 - An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers
AU - Jahangir Alam;Tiago Falk
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5149
ER -
Jahangir Alam,Tiago Falk. (2020). An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers. IEEE SigPort. http://sigport.org/5149
Jahangir Alam,Tiago Falk, 2020. An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers. Available at: http://sigport.org/5149.
Jahangir Alam,Tiago Falk. (2020). "An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers." Web.
1. Jahangir Alam,Tiago Falk. An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5149

A Hybrid Approach for Thermographic Imaging with Deep Learning


We propose a hybrid method for reconstructing thermographic images by combining the recently developed virtual wave concept with deep neural networks. The method can be used to detect defects inside materials in a non-destructive way. We propose two architectures along with a thorough evaluation that shows a substantial improvement compared to state-of-the-art reconstruction procedures. The virtual waves are invariant of the thermal diffusivity property of the material.

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Authors:
Péter Kovács, Bernhard Lehner, Gregor Thummerer, Günther Mayr, Peter Burgholzer, Mario Huemer
Submitted On:
30 April 2020 - 11:14am
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Presentation for ICASSP2020

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[1] Péter Kovács, Bernhard Lehner, Gregor Thummerer, Günther Mayr, Peter Burgholzer, Mario Huemer, "A Hybrid Approach for Thermographic Imaging with Deep Learning", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5115. Accessed: Oct. 26, 2020.
@article{5115-20,
url = {http://sigport.org/5115},
author = {Péter Kovács; Bernhard Lehner; Gregor Thummerer; Günther Mayr; Peter Burgholzer; Mario Huemer },
publisher = {IEEE SigPort},
title = {A Hybrid Approach for Thermographic Imaging with Deep Learning},
year = {2020} }
TY - EJOUR
T1 - A Hybrid Approach for Thermographic Imaging with Deep Learning
AU - Péter Kovács; Bernhard Lehner; Gregor Thummerer; Günther Mayr; Peter Burgholzer; Mario Huemer
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5115
ER -
Péter Kovács, Bernhard Lehner, Gregor Thummerer, Günther Mayr, Peter Burgholzer, Mario Huemer. (2020). A Hybrid Approach for Thermographic Imaging with Deep Learning. IEEE SigPort. http://sigport.org/5115
Péter Kovács, Bernhard Lehner, Gregor Thummerer, Günther Mayr, Peter Burgholzer, Mario Huemer, 2020. A Hybrid Approach for Thermographic Imaging with Deep Learning. Available at: http://sigport.org/5115.
Péter Kovács, Bernhard Lehner, Gregor Thummerer, Günther Mayr, Peter Burgholzer, Mario Huemer. (2020). "A Hybrid Approach for Thermographic Imaging with Deep Learning." Web.
1. Péter Kovács, Bernhard Lehner, Gregor Thummerer, Günther Mayr, Peter Burgholzer, Mario Huemer. A Hybrid Approach for Thermographic Imaging with Deep Learning [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5115

A Self-Attentive Emotion Recognition Network


Attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In this work, we introduce a novel attention mechanism capable of inferring the immensity of the effect of each past utterance on the current speaker emotional state.

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Authors:
Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis, Ilias Leontiades, Sotirios Chatzis
Submitted On:
13 February 2020 - 2:28pm
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Poster__A_Self_Attentive_Emotion_Recognition_Network.pdf

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[1] Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis, Ilias Leontiades, Sotirios Chatzis, "A Self-Attentive Emotion Recognition Network", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4988. Accessed: Oct. 26, 2020.
@article{4988-20,
url = {http://sigport.org/4988},
author = {Harris Partaourides; Kostantinos Papadamou; Nicolas Kourtellis; Ilias Leontiades; Sotirios Chatzis },
publisher = {IEEE SigPort},
title = {A Self-Attentive Emotion Recognition Network},
year = {2020} }
TY - EJOUR
T1 - A Self-Attentive Emotion Recognition Network
AU - Harris Partaourides; Kostantinos Papadamou; Nicolas Kourtellis; Ilias Leontiades; Sotirios Chatzis
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4988
ER -
Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis, Ilias Leontiades, Sotirios Chatzis. (2020). A Self-Attentive Emotion Recognition Network. IEEE SigPort. http://sigport.org/4988
Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis, Ilias Leontiades, Sotirios Chatzis, 2020. A Self-Attentive Emotion Recognition Network. Available at: http://sigport.org/4988.
Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis, Ilias Leontiades, Sotirios Chatzis. (2020). "A Self-Attentive Emotion Recognition Network." Web.
1. Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis, Ilias Leontiades, Sotirios Chatzis. A Self-Attentive Emotion Recognition Network [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4988

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