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Image/Video Coding

CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES


The problem of impulse noise mitigation is considered when videos encoded using a SoftCast based Linear Video Coding scheme are transmitted using an OFDM scheme over a wideband channel prone to impulse noise A Fast Bayesian Matching Pursuit algorithm is employed for impulse noise mitigation This approach requires the provisioning of some OFDM subchannels to estimate the impulse noise locations and amplitudes Provisioned subchannels cannot be used to transmit data and lead to a decrease of the nominal decoded video quality at receivers in absence of impulse noise Using a phenomenological mod

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Authors:
Shuo Zheng, Marco Cagnazzo, Michel Kieffer
Submitted On:
16 May 2019 - 6:45pm
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PPT_ICASSP_2019.pdf

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[1] Shuo Zheng, Marco Cagnazzo, Michel Kieffer, "CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4541. Accessed: Jul. 23, 2019.
@article{4541-19,
url = {http://sigport.org/4541},
author = {Shuo Zheng; Marco Cagnazzo; Michel Kieffer },
publisher = {IEEE SigPort},
title = {CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES},
year = {2019} }
TY - EJOUR
T1 - CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES
AU - Shuo Zheng; Marco Cagnazzo; Michel Kieffer
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4541
ER -
Shuo Zheng, Marco Cagnazzo, Michel Kieffer. (2019). CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES. IEEE SigPort. http://sigport.org/4541
Shuo Zheng, Marco Cagnazzo, Michel Kieffer, 2019. CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES. Available at: http://sigport.org/4541.
Shuo Zheng, Marco Cagnazzo, Michel Kieffer. (2019). "CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES." Web.
1. Shuo Zheng, Marco Cagnazzo, Michel Kieffer. CHANNEL IMPULSIVE NOISE MITIGATION FOR LINEAR VIDEO CODING SCHEMES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4541

CNNs for Intra Prediction using Cross-Component Adaption - Presentation


Presentation Slides for "Convolutional Neural Networks for Video Intra Prediction Using Cross-component Adaptation"

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Authors:
Jonathan Wiesner, Jens Schneider, Christian Rohlfing
Submitted On:
10 May 2019 - 8:45am
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ICASSP_Presentation.pdf

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[1] Jonathan Wiesner, Jens Schneider, Christian Rohlfing, "CNNs for Intra Prediction using Cross-Component Adaption - Presentation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4322. Accessed: Jul. 23, 2019.
@article{4322-19,
url = {http://sigport.org/4322},
author = {Jonathan Wiesner; Jens Schneider; Christian Rohlfing },
publisher = {IEEE SigPort},
title = {CNNs for Intra Prediction using Cross-Component Adaption - Presentation},
year = {2019} }
TY - EJOUR
T1 - CNNs for Intra Prediction using Cross-Component Adaption - Presentation
AU - Jonathan Wiesner; Jens Schneider; Christian Rohlfing
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4322
ER -
Jonathan Wiesner, Jens Schneider, Christian Rohlfing. (2019). CNNs for Intra Prediction using Cross-Component Adaption - Presentation. IEEE SigPort. http://sigport.org/4322
Jonathan Wiesner, Jens Schneider, Christian Rohlfing, 2019. CNNs for Intra Prediction using Cross-Component Adaption - Presentation. Available at: http://sigport.org/4322.
Jonathan Wiesner, Jens Schneider, Christian Rohlfing. (2019). "CNNs for Intra Prediction using Cross-Component Adaption - Presentation." Web.
1. Jonathan Wiesner, Jens Schneider, Christian Rohlfing. CNNs for Intra Prediction using Cross-Component Adaption - Presentation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4322

DSSLIC: Deep Semantic Segmentation-based Layered Image Compression


We propose a deep semantic segmentation-based layered image compression (DSSLIC) framework in which the segmentation map of the input image is obtained and encoded as the base layer of the bit-stream. Experimental results show that the proposed framework outperforms the H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics in RGB domain. Besides, since semantic map is included in the bit-stream, the proposed scheme can facilitate many other tasks such as image search and object-based adaptive image compression.

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Authors:
Jie Liang, Jingning Han
Submitted On:
9 May 2019 - 4:16pm
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DSSLIC_ICASSP_Poster - V2.pdf

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[1] Jie Liang, Jingning Han, "DSSLIC: Deep Semantic Segmentation-based Layered Image Compression", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4236. Accessed: Jul. 23, 2019.
@article{4236-19,
url = {http://sigport.org/4236},
author = {Jie Liang; Jingning Han },
publisher = {IEEE SigPort},
title = {DSSLIC: Deep Semantic Segmentation-based Layered Image Compression},
year = {2019} }
TY - EJOUR
T1 - DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
AU - Jie Liang; Jingning Han
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4236
ER -
Jie Liang, Jingning Han. (2019). DSSLIC: Deep Semantic Segmentation-based Layered Image Compression. IEEE SigPort. http://sigport.org/4236
Jie Liang, Jingning Han, 2019. DSSLIC: Deep Semantic Segmentation-based Layered Image Compression. Available at: http://sigport.org/4236.
Jie Liang, Jingning Han. (2019). "DSSLIC: Deep Semantic Segmentation-based Layered Image Compression." Web.
1. Jie Liang, Jingning Han. DSSLIC: Deep Semantic Segmentation-based Layered Image Compression [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4236

Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding


Scalable lossless video coding is an important aspect for many professional applications. Wavelet-based video coding decomposes an input sequence into a lowpass and a highpass subband by filtering along the temporal axis. The lowpass subband can be used for previewing purposes, while the highpass subband provides the residual content for lossless reconstruction of the original sequence. The recursive application of the wavelet transform to the lowpass subband of the previous stage yields coarser temporal resolutions of the input sequence.

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Authors:
Daniela Lanz, Christian Herbert, André Kaup
Submitted On:
8 May 2019 - 2:35am
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[1] Daniela Lanz, Christian Herbert, André Kaup, "Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4022. Accessed: Jul. 23, 2019.
@article{4022-19,
url = {http://sigport.org/4022},
author = {Daniela Lanz; Christian Herbert; André Kaup },
publisher = {IEEE SigPort},
title = {Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding},
year = {2019} }
TY - EJOUR
T1 - Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding
AU - Daniela Lanz; Christian Herbert; André Kaup
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4022
ER -
Daniela Lanz, Christian Herbert, André Kaup. (2019). Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding. IEEE SigPort. http://sigport.org/4022
Daniela Lanz, Christian Herbert, André Kaup, 2019. Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding. Available at: http://sigport.org/4022.
Daniela Lanz, Christian Herbert, André Kaup. (2019). "Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding." Web.
1. Daniela Lanz, Christian Herbert, André Kaup. Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4022

CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS


Video transrating has become an essential task in streaming service providers that need to transmit and deliver different versions of the same content for a multitude of users that operate under different network conditions. As the transrating operation is comprised of a decoding and an encoding step in sequence, a huge computational cost is required in such large-scale services, especially when considering the use of complex state-of-the-art codecs, such as the High Efficiency Video Coding (HEVC).

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Authors:
Thiago Bubolz, Mateus Grellert
Submitted On:
7 May 2019 - 3:15pm
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[1] Thiago Bubolz, Mateus Grellert, "CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3951. Accessed: Jul. 23, 2019.
@article{3951-19,
url = {http://sigport.org/3951},
author = {Thiago Bubolz; Mateus Grellert },
publisher = {IEEE SigPort},
title = {CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS},
year = {2019} }
TY - EJOUR
T1 - CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS
AU - Thiago Bubolz; Mateus Grellert
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3951
ER -
Thiago Bubolz, Mateus Grellert. (2019). CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS. IEEE SigPort. http://sigport.org/3951
Thiago Bubolz, Mateus Grellert, 2019. CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS. Available at: http://sigport.org/3951.
Thiago Bubolz, Mateus Grellert. (2019). "CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS." Web.
1. Thiago Bubolz, Mateus Grellert. CODING TREE EARLY TERMINATION FOR FAST HEVC TRANSRATING BASED ON RANDOM FORESTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3951

Multiple Linear Regression for High Efficiency Video Intra Coding


In video coding frameworks, the essence of intra coding is leveraging the spatial correlation within a frame to remove redundancy thus achieving compact transmitting data. With modern video acquisition devices improvement, more high-definition videos emerge into people’s lives which has set a new challenge for high-efficiency video coding. In this paper, we propose a novel intra video coding scheme based on Multiple Linear Regression (MLR), named Multiple linear regression Intra Prediction (MIP).

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Authors:
Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu
Submitted On:
7 May 2019 - 2:19pm
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[1] Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu, "Multiple Linear Regression for High Efficiency Video Intra Coding", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3940. Accessed: Jul. 23, 2019.
@article{3940-19,
url = {http://sigport.org/3940},
author = {Zhaobin Zhang; Yue Li; Li Li; Zhu Li; Shan Liu },
publisher = {IEEE SigPort},
title = {Multiple Linear Regression for High Efficiency Video Intra Coding},
year = {2019} }
TY - EJOUR
T1 - Multiple Linear Regression for High Efficiency Video Intra Coding
AU - Zhaobin Zhang; Yue Li; Li Li; Zhu Li; Shan Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3940
ER -
Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu. (2019). Multiple Linear Regression for High Efficiency Video Intra Coding. IEEE SigPort. http://sigport.org/3940
Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu, 2019. Multiple Linear Regression for High Efficiency Video Intra Coding. Available at: http://sigport.org/3940.
Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu. (2019). "Multiple Linear Regression for High Efficiency Video Intra Coding." Web.
1. Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu. Multiple Linear Regression for High Efficiency Video Intra Coding [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3940

Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding

Paper Details

Authors:
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO
Submitted On:
7 May 2019 - 2:14pm
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Poster.pdf

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[1] CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO, "Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3939. Accessed: Jul. 23, 2019.
@article{3939-19,
url = {http://sigport.org/3939},
author = {CHUNBO LUO; GEYONG MIN; WANG MIAO; LIANG WU; TIANXIAO ZHAO },
publisher = {IEEE SigPort},
title = {Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding},
year = {2019} }
TY - EJOUR
T1 - Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding
AU - CHUNBO LUO; GEYONG MIN; WANG MIAO; LIANG WU; TIANXIAO ZHAO
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3939
ER -
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. (2019). Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding. IEEE SigPort. http://sigport.org/3939
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO, 2019. Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding. Available at: http://sigport.org/3939.
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. (2019). "Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding." Web.
1. CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3939

Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding

Paper Details

Authors:
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO
Submitted On:
7 May 2019 - 2:14pm
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Poster.pdf

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[1] CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO, "Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3938. Accessed: Jul. 23, 2019.
@article{3938-19,
url = {http://sigport.org/3938},
author = {CHUNBO LUO; GEYONG MIN; WANG MIAO; LIANG WU; TIANXIAO ZHAO },
publisher = {IEEE SigPort},
title = {Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding},
year = {2019} }
TY - EJOUR
T1 - Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding
AU - CHUNBO LUO; GEYONG MIN; WANG MIAO; LIANG WU; TIANXIAO ZHAO
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3938
ER -
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. (2019). Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding. IEEE SigPort. http://sigport.org/3938
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO, 2019. Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding. Available at: http://sigport.org/3938.
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. (2019). "Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding." Web.
1. CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3938

Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting


With the boosting requirements of realistic 3D modeling for immersive applications, advent of the newly-developed 3D point cloud has attracted great attention. Frankly, immersive experience using high data volume affirms the importance of efficient compression. Inspired by the video-based point cloud compression (V-PCC), we propose a novel point cloud compression algorithm based on polynomial fitting of proper patches. Moreover, the original point cloud is segmented into various patches.

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Authors:
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li
Submitted On:
15 April 2019 - 5:18am
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polynomial poster_24x48.pdf

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[1] Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li, "Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3892. Accessed: Jul. 23, 2019.
@article{3892-19,
url = {http://sigport.org/3892},
author = {Yingzhan Xu; Wenjie Zhu; Yiling Xu; Zhu Li },
publisher = {IEEE SigPort},
title = {Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting},
year = {2019} }
TY - EJOUR
T1 - Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting
AU - Yingzhan Xu; Wenjie Zhu; Yiling Xu; Zhu Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3892
ER -
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. (2019). Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting. IEEE SigPort. http://sigport.org/3892
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li, 2019. Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting. Available at: http://sigport.org/3892.
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. (2019). "Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting." Web.
1. Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3892

DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION


Most covariance-based representations of actions are focused on the statistical features of poses by empirical averaging weighting. Note that these poses have a variety of saliency levels for different actions. Neglecting pose saliency could degrade the discriminative power of the covariance features, and further reduce the performance of action recognition. In this paper, we propose a novel saliency weighting covariance feature representation, Saliency-Pose-Attention Covariance(SPA-Cov), which reduces the negative effects from the ambiguous pose samples.

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Authors:
Zhiyong Feng,Yong Su,Meng Xing
Submitted On:
18 February 2019 - 6:34am
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Poster for conference.

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[1] Zhiyong Feng,Yong Su,Meng Xing, "DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3854. Accessed: Jul. 23, 2019.
@article{3854-19,
url = {http://sigport.org/3854},
author = {Zhiyong Feng;Yong Su;Meng Xing },
publisher = {IEEE SigPort},
title = {DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION
AU - Zhiyong Feng;Yong Su;Meng Xing
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3854
ER -
Zhiyong Feng,Yong Su,Meng Xing. (2019). DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION. IEEE SigPort. http://sigport.org/3854
Zhiyong Feng,Yong Su,Meng Xing, 2019. DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION. Available at: http://sigport.org/3854.
Zhiyong Feng,Yong Su,Meng Xing. (2019). "DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION." Web.
1. Zhiyong Feng,Yong Su,Meng Xing. DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3854

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