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Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning

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Authors:
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald
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21 May 2020 - 1:25am
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VbsNetStokesIcassp.pdf

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[1] Jack W. Stokes, Rakshit Agrawal, Geoff McDonald, "Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5425. Accessed: Aug. 12, 2020.
@article{5425-20,
url = {http://sigport.org/5425},
author = {Jack W. Stokes; Rakshit Agrawal; Geoff McDonald },
publisher = {IEEE SigPort},
title = {Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning},
year = {2020} }
TY - EJOUR
T1 - Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning
AU - Jack W. Stokes; Rakshit Agrawal; Geoff McDonald
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5425
ER -
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald. (2020). Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning. IEEE SigPort. http://sigport.org/5425
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald, 2020. Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning. Available at: http://sigport.org/5425.
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald. (2020). "Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning." Web.
1. Jack W. Stokes, Rakshit Agrawal, Geoff McDonald. Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5425

Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption

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Authors:
Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes
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21 May 2020 - 1:29am
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[1] Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes, "Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5424. Accessed: Aug. 12, 2020.
@article{5424-20,
url = {http://sigport.org/5424},
author = {Edward J. Chou; Arun Gururajan; Kim Laine; Nitin Kumar Goel; Anna Bertiger; Jack W. Stokes },
publisher = {IEEE SigPort},
title = {Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption},
year = {2020} }
TY - EJOUR
T1 - Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption
AU - Edward J. Chou; Arun Gururajan; Kim Laine; Nitin Kumar Goel; Anna Bertiger; Jack W. Stokes
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5424
ER -
Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes. (2020). Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption. IEEE SigPort. http://sigport.org/5424
Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes, 2020. Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption. Available at: http://sigport.org/5424.
Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes. (2020). "Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption." Web.
1. Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes. Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5424

'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection

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Authors:
Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan
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21 May 2020 - 1:35am
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texception_icassp_presentation.pdf

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[1] Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan, "'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5423. Accessed: Aug. 12, 2020.
@article{5423-20,
url = {http://sigport.org/5423},
author = {Farid Tajaddodianfar; Jack W. Stokes; Arun Gururajan },
publisher = {IEEE SigPort},
title = {'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection},
year = {2020} }
TY - EJOUR
T1 - 'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection
AU - Farid Tajaddodianfar; Jack W. Stokes; Arun Gururajan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5423
ER -
Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan. (2020). 'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection. IEEE SigPort. http://sigport.org/5423
Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan, 2020. 'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection. Available at: http://sigport.org/5423.
Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan. (2020). "'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection." Web.
1. Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan. 'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5423

SQUAREMIX: A Faster Pseudorandom Number Generator for Dynamic-Multithreading Platforms

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30 March 2020 - 10:30pm
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DCC_Poster__Ritchie_Bibak.pdf

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[1] , "SQUAREMIX: A Faster Pseudorandom Number Generator for Dynamic-Multithreading Platforms", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5068. Accessed: Aug. 12, 2020.
@article{5068-20,
url = {http://sigport.org/5068},
author = { },
publisher = {IEEE SigPort},
title = {SQUAREMIX: A Faster Pseudorandom Number Generator for Dynamic-Multithreading Platforms},
year = {2020} }
TY - EJOUR
T1 - SQUAREMIX: A Faster Pseudorandom Number Generator for Dynamic-Multithreading Platforms
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5068
ER -
. (2020). SQUAREMIX: A Faster Pseudorandom Number Generator for Dynamic-Multithreading Platforms. IEEE SigPort. http://sigport.org/5068
, 2020. SQUAREMIX: A Faster Pseudorandom Number Generator for Dynamic-Multithreading Platforms. Available at: http://sigport.org/5068.
. (2020). "SQUAREMIX: A Faster Pseudorandom Number Generator for Dynamic-Multithreading Platforms." Web.
1. . SQUAREMIX: A Faster Pseudorandom Number Generator for Dynamic-Multithreading Platforms [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5068

END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET


In video surveillance applications, person search is a chal-
lenging task consisting in detecting people and extracting
features from their silhouette for re-identification (re-ID) pur-
pose. We propose a new end-to-end model that jointly com-
putes detection and feature extraction steps through a single
deep Convolutional Neural Network architecture. Sharing
feature maps between the two tasks for jointly describing
people commonalities and specificities allows faster runtime,
which is valuable in real-world applications. In addition

Paper Details

Authors:
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier
Submitted On:
19 September 2019 - 12:16pm
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2019_ICIP_aloesch

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[1] Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier, "END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4743. Accessed: Aug. 12, 2020.
@article{4743-19,
url = {http://sigport.org/4743},
author = {Angelique Loesch; Jaonary Rabarisoa; Romaric Audigier },
publisher = {IEEE SigPort},
title = {END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET},
year = {2019} }
TY - EJOUR
T1 - END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET
AU - Angelique Loesch; Jaonary Rabarisoa; Romaric Audigier
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4743
ER -
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier. (2019). END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET. IEEE SigPort. http://sigport.org/4743
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier, 2019. END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET. Available at: http://sigport.org/4743.
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier. (2019). "END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET." Web.
1. Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier. END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4743

A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS


We describe in this paper a Two-Stream Siamese Neural Network for vehicle re-identification. The proposed network is fed simultaneously with small coarse patches of the vehicle shape’s, with 96 × 96 pixels, in one stream, and fine features extracted from license plate patches, easily readable by humans, with 96 × 48 pixels, in the other one.

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Authors:
Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto
Submitted On:
19 September 2019 - 6:09am
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ICIP 2019.pdf

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[1] Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto, "A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4719. Accessed: Aug. 12, 2020.
@article{4719-19,
url = {http://sigport.org/4719},
author = {Icaro Oliveira; Keiko Fonseca; Rodrigo Minetto },
publisher = {IEEE SigPort},
title = {A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS},
year = {2019} }
TY - EJOUR
T1 - A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS
AU - Icaro Oliveira; Keiko Fonseca; Rodrigo Minetto
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4719
ER -
Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto. (2019). A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS. IEEE SigPort. http://sigport.org/4719
Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto, 2019. A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS. Available at: http://sigport.org/4719.
Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto. (2019). "A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS." Web.
1. Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto. A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4719

Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)


Given a sequence of observations for each person in each camera, identifying or re-identifying the same person across different cameras is one of the objectives of video surveillance systems. In the case of video based person re-id, the challenge is to handle the high correlation between temporally adjacent frames. The presence of non-informative frames results in high redundancy which needs to be removed for an efficient re-id.

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Authors:
Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty
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19 September 2019 - 6:40am
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ICIP_2019_POSTER_(PAPER_ID_3410).pdf

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[1] Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty, "Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4691. Accessed: Aug. 12, 2020.
@article{4691-19,
url = {http://sigport.org/4691},
author = {Gaurav Kumar Nayak; Utkarsh Shreemali; R Venkatesh Babu; Anirban Chakraborty },
publisher = {IEEE SigPort},
title = {Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)},
year = {2019} }
TY - EJOUR
T1 - Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)
AU - Gaurav Kumar Nayak; Utkarsh Shreemali; R Venkatesh Babu; Anirban Chakraborty
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4691
ER -
Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty. (2019). Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP). IEEE SigPort. http://sigport.org/4691
Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty, 2019. Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP). Available at: http://sigport.org/4691.
Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty. (2019). "Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)." Web.
1. Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty. Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP) [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4691

Differentially Private Sparse Inverse Covariance Estimation

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Authors:
Mengdi Huai, Jinhui Xu
Submitted On:
20 November 2018 - 4:05pm
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private_sparse_inverse.pdf

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[1] Mengdi Huai, Jinhui Xu, "Differentially Private Sparse Inverse Covariance Estimation ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3694. Accessed: Aug. 12, 2020.
@article{3694-18,
url = {http://sigport.org/3694},
author = {Mengdi Huai; Jinhui Xu },
publisher = {IEEE SigPort},
title = {Differentially Private Sparse Inverse Covariance Estimation },
year = {2018} }
TY - EJOUR
T1 - Differentially Private Sparse Inverse Covariance Estimation
AU - Mengdi Huai; Jinhui Xu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3694
ER -
Mengdi Huai, Jinhui Xu. (2018). Differentially Private Sparse Inverse Covariance Estimation . IEEE SigPort. http://sigport.org/3694
Mengdi Huai, Jinhui Xu, 2018. Differentially Private Sparse Inverse Covariance Estimation . Available at: http://sigport.org/3694.
Mengdi Huai, Jinhui Xu. (2018). "Differentially Private Sparse Inverse Covariance Estimation ." Web.
1. Mengdi Huai, Jinhui Xu. Differentially Private Sparse Inverse Covariance Estimation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3694

USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY

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Authors:
R. Soundar Raja James, K. Naik and A. Nayak
Submitted On:
23 April 2018 - 12:51pm
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ICASSP2018-Poster-Albasir.v2.pdf

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[1] R. Soundar Raja James, K. Naik and A. Nayak, "USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3151. Accessed: Aug. 12, 2020.
@article{3151-18,
url = {http://sigport.org/3151},
author = {R. Soundar Raja James; K. Naik and A. Nayak },
publisher = {IEEE SigPort},
title = {USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY},
year = {2018} }
TY - EJOUR
T1 - USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY
AU - R. Soundar Raja James; K. Naik and A. Nayak
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3151
ER -
R. Soundar Raja James, K. Naik and A. Nayak. (2018). USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY. IEEE SigPort. http://sigport.org/3151
R. Soundar Raja James, K. Naik and A. Nayak, 2018. USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY. Available at: http://sigport.org/3151.
R. Soundar Raja James, K. Naik and A. Nayak. (2018). "USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY." Web.
1. R. Soundar Raja James, K. Naik and A. Nayak. USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3151

Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss


In this paper, we propose deep feature embedding learning for person re-identification (re-id) using lifted structured loss. Although triplet loss has been commonly used in deep neural networks for person re-id, the triplet loss-based framework is not effective in fully using the batch information. Thus, it needs to choose hard negative samples manually that is very time-consuming. To address this problem, we adopt lifted structured loss for deep neural networks that makes the network learn better feature embedding by minimizing intra-class variation and maximizing inter-class variation.

Paper Details

Authors:
Zhangping He, Zhendong Zhang, Cheolkon Jung
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20 April 2018 - 5:23am
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ICASSP2018_PersonReID_final.pdf

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[1] Zhangping He, Zhendong Zhang, Cheolkon Jung, "Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3098. Accessed: Aug. 12, 2020.
@article{3098-18,
url = {http://sigport.org/3098},
author = {Zhangping He; Zhendong Zhang; Cheolkon Jung },
publisher = {IEEE SigPort},
title = {Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss},
year = {2018} }
TY - EJOUR
T1 - Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss
AU - Zhangping He; Zhendong Zhang; Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3098
ER -
Zhangping He, Zhendong Zhang, Cheolkon Jung. (2018). Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss. IEEE SigPort. http://sigport.org/3098
Zhangping He, Zhendong Zhang, Cheolkon Jung, 2018. Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss. Available at: http://sigport.org/3098.
Zhangping He, Zhendong Zhang, Cheolkon Jung. (2018). "Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss." Web.
1. Zhangping He, Zhendong Zhang, Cheolkon Jung. Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3098

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