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SIDAS‘17

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Authors:
Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen
Submitted On:
11 October 2017 - 2:41pm
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SIDAS2017_materials.pdf

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[1] Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen, "SIDAS‘17", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2257. Accessed: Nov. 17, 2018.
@article{2257-17,
url = {http://sigport.org/2257},
author = {Changshui Zhang; Shefeng Yan; Liwei Wang; Shiguang Shan; Jingdong Chen; Fangjiong Chen },
publisher = {IEEE SigPort},
title = {SIDAS‘17},
year = {2017} }
TY - EJOUR
T1 - SIDAS‘17
AU - Changshui Zhang; Shefeng Yan; Liwei Wang; Shiguang Shan; Jingdong Chen; Fangjiong Chen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2257
ER -
Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen. (2017). SIDAS‘17. IEEE SigPort. http://sigport.org/2257
Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen, 2017. SIDAS‘17. Available at: http://sigport.org/2257.
Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen. (2017). "SIDAS‘17." Web.
1. Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen. SIDAS‘17 [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2257

Unsupervised Detection of Periodic Segments in Videos


We present a solution to the problem of discovering all periodic
segments of a video and of estimating their period in
a completely unsupervised manner. These segments may be
located anywhere in the video, may differ in duration, speed,
period and may represent unseen motion patterns of any type
of objects (e.g., humans, animals, machines, etc). The proposed
method capitalizes on earlier research on the problem
of detecting common actions in videos, also known as commonality
detection or video co-segmentation. The proposed

Paper Details

Authors:
Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros
Submitted On:
5 October 2018 - 4:02am
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Unsupervised Detection of Periodic Segments in Videos

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[1] Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros, "Unsupervised Detection of Periodic Segments in Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3516. Accessed: Nov. 17, 2018.
@article{3516-18,
url = {http://sigport.org/3516},
author = {Costas Panagiotakis; Giorgos Karvounas; Antonis A. Argyros },
publisher = {IEEE SigPort},
title = {Unsupervised Detection of Periodic Segments in Videos},
year = {2018} }
TY - EJOUR
T1 - Unsupervised Detection of Periodic Segments in Videos
AU - Costas Panagiotakis; Giorgos Karvounas; Antonis A. Argyros
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3516
ER -
Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros. (2018). Unsupervised Detection of Periodic Segments in Videos. IEEE SigPort. http://sigport.org/3516
Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros, 2018. Unsupervised Detection of Periodic Segments in Videos. Available at: http://sigport.org/3516.
Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros. (2018). "Unsupervised Detection of Periodic Segments in Videos." Web.
1. Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros. Unsupervised Detection of Periodic Segments in Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3516

S3D: Stacking Segmental P3D for Action Quality Assessment


Action quality assessment is crucial in areas of sports, surgery and assembly line where action skills can be evaluated. In this paper, we propose the Segment-based P3D-fused network S3D built-upon ED-TCN and push the performance on the UNLV-Dive dataset by a significant margin. We verify that segment-aware training performs better than full-video training which turns out to focus on the water spray. We show that temporal segmentation can be embedded with few efforts.

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Authors:
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran
Submitted On:
5 October 2018 - 2:08am
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AI Referee: Score Olympic Games

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[1] Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran, "S3D: Stacking Segmental P3D for Action Quality Assessment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3501. Accessed: Nov. 17, 2018.
@article{3501-18,
url = {http://sigport.org/3501},
author = {Ye Tian; Austin Reiter; Gregory D. Hager; Trac D. Tran },
publisher = {IEEE SigPort},
title = {S3D: Stacking Segmental P3D for Action Quality Assessment},
year = {2018} }
TY - EJOUR
T1 - S3D: Stacking Segmental P3D for Action Quality Assessment
AU - Ye Tian; Austin Reiter; Gregory D. Hager; Trac D. Tran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3501
ER -
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. (2018). S3D: Stacking Segmental P3D for Action Quality Assessment. IEEE SigPort. http://sigport.org/3501
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran, 2018. S3D: Stacking Segmental P3D for Action Quality Assessment. Available at: http://sigport.org/3501.
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. (2018). "S3D: Stacking Segmental P3D for Action Quality Assessment." Web.
1. Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. S3D: Stacking Segmental P3D for Action Quality Assessment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3501

BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES

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Authors:
D. Fabrice ATREVI, Damien VIVET, Bruno EMILE
Submitted On:
20 April 2018 - 1:06am
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Poster_Icassp_ATREVI.pdf

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[1] D. Fabrice ATREVI, Damien VIVET, Bruno EMILE, "BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3071. Accessed: Nov. 17, 2018.
@article{3071-18,
url = {http://sigport.org/3071},
author = { D. Fabrice ATREVI; Damien VIVET; Bruno EMILE },
publisher = {IEEE SigPort},
title = {BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES},
year = {2018} }
TY - EJOUR
T1 - BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES
AU - D. Fabrice ATREVI; Damien VIVET; Bruno EMILE
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3071
ER -
D. Fabrice ATREVI, Damien VIVET, Bruno EMILE. (2018). BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES. IEEE SigPort. http://sigport.org/3071
D. Fabrice ATREVI, Damien VIVET, Bruno EMILE, 2018. BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES. Available at: http://sigport.org/3071.
D. Fabrice ATREVI, Damien VIVET, Bruno EMILE. (2018). "BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES." Web.
1. D. Fabrice ATREVI, Damien VIVET, Bruno EMILE. BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3071

When Harmonic Analysis Meets Machine Learning: Lipschitz Analysis of Deep Convolution Networks


Deep neural networks have led to dramatic improvements in performance for many machine learning tasks, yet the mathematical reasons for this success remain largely unclear. In this talk we present recent developments in the mathematical framework of convolutive neural networks (CNN). In particular we discuss the scattering network of Mallat and how it relates to another problem in harmonic analysis, namely the phase retrieval problem. Then we discuss the general convolutive neural network from a theoretician point of view.

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Authors:
Radu Balan
Submitted On:
19 October 2017 - 11:56am
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Presentation slides (pdf version)

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[1] Radu Balan, "When Harmonic Analysis Meets Machine Learning: Lipschitz Analysis of Deep Convolution Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2263. Accessed: Nov. 17, 2018.
@article{2263-17,
url = {http://sigport.org/2263},
author = {Radu Balan },
publisher = {IEEE SigPort},
title = {When Harmonic Analysis Meets Machine Learning: Lipschitz Analysis of Deep Convolution Networks},
year = {2017} }
TY - EJOUR
T1 - When Harmonic Analysis Meets Machine Learning: Lipschitz Analysis of Deep Convolution Networks
AU - Radu Balan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2263
ER -
Radu Balan. (2017). When Harmonic Analysis Meets Machine Learning: Lipschitz Analysis of Deep Convolution Networks. IEEE SigPort. http://sigport.org/2263
Radu Balan, 2017. When Harmonic Analysis Meets Machine Learning: Lipschitz Analysis of Deep Convolution Networks. Available at: http://sigport.org/2263.
Radu Balan. (2017). "When Harmonic Analysis Meets Machine Learning: Lipschitz Analysis of Deep Convolution Networks." Web.
1. Radu Balan. When Harmonic Analysis Meets Machine Learning: Lipschitz Analysis of Deep Convolution Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2263

SIDAS‘17

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Authors:
Submitted On:
8 October 2017 - 10:24pm
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SIDAS2017_materials.pdf

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[1] , "SIDAS‘17", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2256. Accessed: Nov. 17, 2018.
@article{2256-17,
url = {http://sigport.org/2256},
author = { },
publisher = {IEEE SigPort},
title = {SIDAS‘17},
year = {2017} }
TY - EJOUR
T1 - SIDAS‘17
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2256
ER -
. (2017). SIDAS‘17. IEEE SigPort. http://sigport.org/2256
, 2017. SIDAS‘17. Available at: http://sigport.org/2256.
. (2017). "SIDAS‘17." Web.
1. . SIDAS‘17 [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2256

ICIP 2017 - SPS Welcoming Remarks, Rabab Ward, IEEE SPS President


IEEE Signal Processing Society welcoming remarks slides from IEEE President, Rabab Ward at ICIP 2017 on 18 September 2017 in Beijing, China.

Accompanying video can be fount on the Resource Center as well as IEEE SPS YouTube: https://www.youtube.com/watch?v=NRRzs0bB0a0.

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Submitted On:
18 September 2017 - 3:38pm
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ICIP 2017 - Rabab Ward Opening Remarks.pdf

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[1] , "ICIP 2017 - SPS Welcoming Remarks, Rabab Ward, IEEE SPS President", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2228. Accessed: Nov. 17, 2018.
@article{2228-17,
url = {http://sigport.org/2228},
author = { },
publisher = {IEEE SigPort},
title = {ICIP 2017 - SPS Welcoming Remarks, Rabab Ward, IEEE SPS President},
year = {2017} }
TY - EJOUR
T1 - ICIP 2017 - SPS Welcoming Remarks, Rabab Ward, IEEE SPS President
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2228
ER -
. (2017). ICIP 2017 - SPS Welcoming Remarks, Rabab Ward, IEEE SPS President. IEEE SigPort. http://sigport.org/2228
, 2017. ICIP 2017 - SPS Welcoming Remarks, Rabab Ward, IEEE SPS President. Available at: http://sigport.org/2228.
. (2017). "ICIP 2017 - SPS Welcoming Remarks, Rabab Ward, IEEE SPS President." Web.
1. . ICIP 2017 - SPS Welcoming Remarks, Rabab Ward, IEEE SPS President [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2228

DenseNet for Dense Flow


Efficient Large-Scale Video Understanding in The Wild

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Authors:
Yi Zhu,Shawn Newsam
Submitted On:
16 September 2017 - 2:54am
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ICIP17_phd_forum_poster.pdf

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[1] Yi Zhu,Shawn Newsam, "DenseNet for Dense Flow", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2182. Accessed: Nov. 17, 2018.
@article{2182-17,
url = {http://sigport.org/2182},
author = {Yi Zhu;Shawn Newsam },
publisher = {IEEE SigPort},
title = {DenseNet for Dense Flow},
year = {2017} }
TY - EJOUR
T1 - DenseNet for Dense Flow
AU - Yi Zhu;Shawn Newsam
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2182
ER -
Yi Zhu,Shawn Newsam. (2017). DenseNet for Dense Flow. IEEE SigPort. http://sigport.org/2182
Yi Zhu,Shawn Newsam, 2017. DenseNet for Dense Flow. Available at: http://sigport.org/2182.
Yi Zhu,Shawn Newsam. (2017). "DenseNet for Dense Flow." Web.
1. Yi Zhu,Shawn Newsam. DenseNet for Dense Flow [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2182

CASCADED TEMPORAL SPATIAL FEATURES FOR VIDEO ACTION RECOGNITION


Extracting spatial-temporal descriptors is a challenging task for video-based human action recognition. We decouple the 3D volume of video frames directly into a cascaded temporal spatial domain via a new convolutional architecture. The motivation behind this design is to achieve deep nonlinear feature representations with reduced network parameters. First, a 1D temporal network with shared parameters is first constructed to map the video sequences along the time axis into feature maps in temporal domain.

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Authors:
Tingzhao Yu, Huxiang Gu, Lingfeng Wang, Shiming Xiang, Chunhong Pan
Submitted On:
15 September 2017 - 5:00am
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CTS_Tsingzao_v1_pdf.pdf

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[1] Tingzhao Yu, Huxiang Gu, Lingfeng Wang, Shiming Xiang, Chunhong Pan, "CASCADED TEMPORAL SPATIAL FEATURES FOR VIDEO ACTION RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2111. Accessed: Nov. 17, 2018.
@article{2111-17,
url = {http://sigport.org/2111},
author = {Tingzhao Yu; Huxiang Gu; Lingfeng Wang; Shiming Xiang; Chunhong Pan },
publisher = {IEEE SigPort},
title = {CASCADED TEMPORAL SPATIAL FEATURES FOR VIDEO ACTION RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - CASCADED TEMPORAL SPATIAL FEATURES FOR VIDEO ACTION RECOGNITION
AU - Tingzhao Yu; Huxiang Gu; Lingfeng Wang; Shiming Xiang; Chunhong Pan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2111
ER -
Tingzhao Yu, Huxiang Gu, Lingfeng Wang, Shiming Xiang, Chunhong Pan. (2017). CASCADED TEMPORAL SPATIAL FEATURES FOR VIDEO ACTION RECOGNITION. IEEE SigPort. http://sigport.org/2111
Tingzhao Yu, Huxiang Gu, Lingfeng Wang, Shiming Xiang, Chunhong Pan, 2017. CASCADED TEMPORAL SPATIAL FEATURES FOR VIDEO ACTION RECOGNITION. Available at: http://sigport.org/2111.
Tingzhao Yu, Huxiang Gu, Lingfeng Wang, Shiming Xiang, Chunhong Pan. (2017). "CASCADED TEMPORAL SPATIAL FEATURES FOR VIDEO ACTION RECOGNITION." Web.
1. Tingzhao Yu, Huxiang Gu, Lingfeng Wang, Shiming Xiang, Chunhong Pan. CASCADED TEMPORAL SPATIAL FEATURES FOR VIDEO ACTION RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2111

Action Recognition In RGB-D Egocentric Videos


In this paper, we investigate the problem of action recognition in RGB-D egocentric videos. These self-generated and embodied videos provide richer semantic cues than the conventional videos captured from the third-person view for action recognition. Moreover, they contain both appearance information and 3D structure of the scenes from the RGB modality and depth modality respectively. Motivated by these advantages,

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Authors:
Yansong Tang, Yi Tian, Jiwen Lu, Jianjiang Feng, Jie Zhou
Submitted On:
6 September 2017 - 9:35am
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ICIP_poster.pdf

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[1] Yansong Tang, Yi Tian, Jiwen Lu, Jianjiang Feng, Jie Zhou, "Action Recognition In RGB-D Egocentric Videos", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1842. Accessed: Nov. 17, 2018.
@article{1842-17,
url = {http://sigport.org/1842},
author = {Yansong Tang; Yi Tian; Jiwen Lu; Jianjiang Feng; Jie Zhou },
publisher = {IEEE SigPort},
title = {Action Recognition In RGB-D Egocentric Videos},
year = {2017} }
TY - EJOUR
T1 - Action Recognition In RGB-D Egocentric Videos
AU - Yansong Tang; Yi Tian; Jiwen Lu; Jianjiang Feng; Jie Zhou
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1842
ER -
Yansong Tang, Yi Tian, Jiwen Lu, Jianjiang Feng, Jie Zhou. (2017). Action Recognition In RGB-D Egocentric Videos. IEEE SigPort. http://sigport.org/1842
Yansong Tang, Yi Tian, Jiwen Lu, Jianjiang Feng, Jie Zhou, 2017. Action Recognition In RGB-D Egocentric Videos. Available at: http://sigport.org/1842.
Yansong Tang, Yi Tian, Jiwen Lu, Jianjiang Feng, Jie Zhou. (2017). "Action Recognition In RGB-D Egocentric Videos." Web.
1. Yansong Tang, Yi Tian, Jiwen Lu, Jianjiang Feng, Jie Zhou. Action Recognition In RGB-D Egocentric Videos [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1842

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