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Neural network learning (MLR-NNLR)

AI: A Signal Processing Perspective


The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), especially since 2010 or so, yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation, and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size.

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Authors:
Brian M. Sadler
Submitted On:
1 December 2017 - 9:19pm
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Presentation slides (pdf version)

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[1] Brian M. Sadler, "AI: A Signal Processing Perspective", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2370. Accessed: Feb. 24, 2018.
@article{2370-17,
url = {http://sigport.org/2370},
author = {Brian M. Sadler },
publisher = {IEEE SigPort},
title = {AI: A Signal Processing Perspective},
year = {2017} }
TY - EJOUR
T1 - AI: A Signal Processing Perspective
AU - Brian M. Sadler
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2370
ER -
Brian M. Sadler. (2017). AI: A Signal Processing Perspective. IEEE SigPort. http://sigport.org/2370
Brian M. Sadler, 2017. AI: A Signal Processing Perspective. Available at: http://sigport.org/2370.
Brian M. Sadler. (2017). "AI: A Signal Processing Perspective." Web.
1. Brian M. Sadler. AI: A Signal Processing Perspective [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2370

Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs

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Authors:
Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin
Submitted On:
13 November 2017 - 12:29pm
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slides.pdf

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[1] Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin, "Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2339. Accessed: Feb. 24, 2018.
@article{2339-17,
url = {http://sigport.org/2339},
author = {Jean-Charles Vialatte; Vincent Gripon; Gilles Coppin },
publisher = {IEEE SigPort},
title = {Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs},
year = {2017} }
TY - EJOUR
T1 - Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs
AU - Jean-Charles Vialatte; Vincent Gripon; Gilles Coppin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2339
ER -
Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin. (2017). Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs. IEEE SigPort. http://sigport.org/2339
Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin, 2017. Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs. Available at: http://sigport.org/2339.
Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin. (2017). "Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs." Web.
1. Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin. Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2339

ARTERY/VEIN CLASSIFICATION IN FUNDUS IMAGES USING CNN AND LIKELIHOOD SCORE PROPAGATION


Artery/vein classification in fundus images is a prerequisite for the assessment of diseases such as diabetes, hypertension or other cardiovascular pathologies. One clinical measure used to assess the severity of cardiovascular risk is the retinal arterio-venous ratio (AVR), which significantly depends on the accuracy of vessel classification into arteries or veins. This paper proposes a novel method for artery/vein classification combining deep learning and graph propagation strategies.

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Authors:
Fantin Girard, Farida Cheriet
Submitted On:
11 November 2017 - 10:33am
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GlobalSIP 2017 slides

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[1] Fantin Girard, Farida Cheriet, "ARTERY/VEIN CLASSIFICATION IN FUNDUS IMAGES USING CNN AND LIKELIHOOD SCORE PROPAGATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2307. Accessed: Feb. 24, 2018.
@article{2307-17,
url = {http://sigport.org/2307},
author = {Fantin Girard; Farida Cheriet },
publisher = {IEEE SigPort},
title = {ARTERY/VEIN CLASSIFICATION IN FUNDUS IMAGES USING CNN AND LIKELIHOOD SCORE PROPAGATION},
year = {2017} }
TY - EJOUR
T1 - ARTERY/VEIN CLASSIFICATION IN FUNDUS IMAGES USING CNN AND LIKELIHOOD SCORE PROPAGATION
AU - Fantin Girard; Farida Cheriet
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2307
ER -
Fantin Girard, Farida Cheriet. (2017). ARTERY/VEIN CLASSIFICATION IN FUNDUS IMAGES USING CNN AND LIKELIHOOD SCORE PROPAGATION. IEEE SigPort. http://sigport.org/2307
Fantin Girard, Farida Cheriet, 2017. ARTERY/VEIN CLASSIFICATION IN FUNDUS IMAGES USING CNN AND LIKELIHOOD SCORE PROPAGATION. Available at: http://sigport.org/2307.
Fantin Girard, Farida Cheriet. (2017). "ARTERY/VEIN CLASSIFICATION IN FUNDUS IMAGES USING CNN AND LIKELIHOOD SCORE PROPAGATION." Web.
1. Fantin Girard, Farida Cheriet. ARTERY/VEIN CLASSIFICATION IN FUNDUS IMAGES USING CNN AND LIKELIHOOD SCORE PROPAGATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2307

Multiple-image Super Resolution Using Both Reconstruction Optimization and Deep Neural Network


We present an efficient multi-image super resolution (MISR) method. Our solution consists of a L1-norm optimized reconstruction scheme for super resolution (SR), and a three-layer convolutional network for artifacts removal, in a concatenated fashion. Such a two-stage method achieves excellent performance, which outperforms the existing state-of-the-art SR methods in both subjective and objective measurements (e.g., 5 to 7 dB improvements on popular image database using PSNR metric).

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Authors:
Jie Wu, Tao Yue, Qiu Shen, Xun Cao, Zhan Ma
Submitted On:
9 November 2017 - 10:11pm
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Super-resolution

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[1] Jie Wu, Tao Yue, Qiu Shen, Xun Cao, Zhan Ma, "Multiple-image Super Resolution Using Both Reconstruction Optimization and Deep Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2279. Accessed: Feb. 24, 2018.
@article{2279-17,
url = {http://sigport.org/2279},
author = {Jie Wu; Tao Yue; Qiu Shen; Xun Cao; Zhan Ma },
publisher = {IEEE SigPort},
title = {Multiple-image Super Resolution Using Both Reconstruction Optimization and Deep Neural Network},
year = {2017} }
TY - EJOUR
T1 - Multiple-image Super Resolution Using Both Reconstruction Optimization and Deep Neural Network
AU - Jie Wu; Tao Yue; Qiu Shen; Xun Cao; Zhan Ma
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2279
ER -
Jie Wu, Tao Yue, Qiu Shen, Xun Cao, Zhan Ma. (2017). Multiple-image Super Resolution Using Both Reconstruction Optimization and Deep Neural Network. IEEE SigPort. http://sigport.org/2279
Jie Wu, Tao Yue, Qiu Shen, Xun Cao, Zhan Ma, 2017. Multiple-image Super Resolution Using Both Reconstruction Optimization and Deep Neural Network. Available at: http://sigport.org/2279.
Jie Wu, Tao Yue, Qiu Shen, Xun Cao, Zhan Ma. (2017). "Multiple-image Super Resolution Using Both Reconstruction Optimization and Deep Neural Network." Web.
1. Jie Wu, Tao Yue, Qiu Shen, Xun Cao, Zhan Ma. Multiple-image Super Resolution Using Both Reconstruction Optimization and Deep Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2279

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: Feb. 24, 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

THE WITS INTELLIGENT TEACHING SYSTEM: DETECTING STUDENT ENGAGEMENT DURING LECTURES USING CONVOLUTIONAL NEURAL NETWORKS

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Authors:
Turgay Celik
Submitted On:
17 September 2017 - 9:18pm
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Klein-Poster.pdf

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[1] Turgay Celik, "THE WITS INTELLIGENT TEACHING SYSTEM: DETECTING STUDENT ENGAGEMENT DURING LECTURES USING CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2217. Accessed: Feb. 24, 2018.
@article{2217-17,
url = {http://sigport.org/2217},
author = {Turgay Celik },
publisher = {IEEE SigPort},
title = {THE WITS INTELLIGENT TEACHING SYSTEM: DETECTING STUDENT ENGAGEMENT DURING LECTURES USING CONVOLUTIONAL NEURAL NETWORKS},
year = {2017} }
TY - EJOUR
T1 - THE WITS INTELLIGENT TEACHING SYSTEM: DETECTING STUDENT ENGAGEMENT DURING LECTURES USING CONVOLUTIONAL NEURAL NETWORKS
AU - Turgay Celik
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2217
ER -
Turgay Celik. (2017). THE WITS INTELLIGENT TEACHING SYSTEM: DETECTING STUDENT ENGAGEMENT DURING LECTURES USING CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/2217
Turgay Celik, 2017. THE WITS INTELLIGENT TEACHING SYSTEM: DETECTING STUDENT ENGAGEMENT DURING LECTURES USING CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/2217.
Turgay Celik. (2017). "THE WITS INTELLIGENT TEACHING SYSTEM: DETECTING STUDENT ENGAGEMENT DURING LECTURES USING CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Turgay Celik. THE WITS INTELLIGENT TEACHING SYSTEM: DETECTING STUDENT ENGAGEMENT DURING LECTURES USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2217

DenseNet for Dense Flow


Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems. In this paper, we investigate a new deep architecture, Densely Connected Convolutional Networks (DenseNet), to learn optical flow. This specific architecture is ideal for the problem at hand as it provides shortcut connections throughout the network, which leads to implicit deep supervision.

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Authors:
Yi Zhu,Shawn Newsam
Submitted On:
16 September 2017 - 2:45am
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ICIP17_paper2550_slides_yizhu.pdf

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[1] Yi Zhu,Shawn Newsam, "DenseNet for Dense Flow", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2181. Accessed: Feb. 24, 2018.
@article{2181-17,
url = {http://sigport.org/2181},
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/2181
ER -
Yi Zhu,Shawn Newsam. (2017). DenseNet for Dense Flow. IEEE SigPort. http://sigport.org/2181
Yi Zhu,Shawn Newsam, 2017. DenseNet for Dense Flow. Available at: http://sigport.org/2181.
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/2181

TOWARDS THINNER CONVOLUTIONAL NEURAL NETWORKS THROUGH GRADUALLY GLOBAL PRUNING


Deep network pruning is an effective method to reduce the storage and computation cost of deep neural networks when applying them to resource-limited devices. Among many pruning granularities, neuron level pruning will remove redundant neurons and filters in the model and result in thinner networks. In this paper, we propose a gradually global pruning scheme for neuron level pruning. In each pruning step,

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Authors:
Zhengtao Wang, Ce Zhu, Zhiqiang Xia, Qi Guo, Yipeng Liu
Submitted On:
15 September 2017 - 1:19pm
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ICIP1701

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[1] Zhengtao Wang, Ce Zhu, Zhiqiang Xia, Qi Guo, Yipeng Liu, " TOWARDS THINNER CONVOLUTIONAL NEURAL NETWORKS THROUGH GRADUALLY GLOBAL PRUNING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2155. Accessed: Feb. 24, 2018.
@article{2155-17,
url = {http://sigport.org/2155},
author = {Zhengtao Wang; Ce Zhu; Zhiqiang Xia; Qi Guo; Yipeng Liu },
publisher = {IEEE SigPort},
title = { TOWARDS THINNER CONVOLUTIONAL NEURAL NETWORKS THROUGH GRADUALLY GLOBAL PRUNING},
year = {2017} }
TY - EJOUR
T1 - TOWARDS THINNER CONVOLUTIONAL NEURAL NETWORKS THROUGH GRADUALLY GLOBAL PRUNING
AU - Zhengtao Wang; Ce Zhu; Zhiqiang Xia; Qi Guo; Yipeng Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2155
ER -
Zhengtao Wang, Ce Zhu, Zhiqiang Xia, Qi Guo, Yipeng Liu. (2017). TOWARDS THINNER CONVOLUTIONAL NEURAL NETWORKS THROUGH GRADUALLY GLOBAL PRUNING. IEEE SigPort. http://sigport.org/2155
Zhengtao Wang, Ce Zhu, Zhiqiang Xia, Qi Guo, Yipeng Liu, 2017. TOWARDS THINNER CONVOLUTIONAL NEURAL NETWORKS THROUGH GRADUALLY GLOBAL PRUNING. Available at: http://sigport.org/2155.
Zhengtao Wang, Ce Zhu, Zhiqiang Xia, Qi Guo, Yipeng Liu. (2017). " TOWARDS THINNER CONVOLUTIONAL NEURAL NETWORKS THROUGH GRADUALLY GLOBAL PRUNING." Web.
1. Zhengtao Wang, Ce Zhu, Zhiqiang Xia, Qi Guo, Yipeng Liu. TOWARDS THINNER CONVOLUTIONAL NEURAL NETWORKS THROUGH GRADUALLY GLOBAL PRUNING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2155

ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING

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Authors:
Qi Guo, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, Yipeng Liu
Submitted On:
15 September 2017 - 11:50am
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ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING.pdf

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[1] Qi Guo, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, Yipeng Liu , "ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2146. Accessed: Feb. 24, 2018.
@article{2146-17,
url = {http://sigport.org/2146},
author = {Qi Guo; Ce Zhu; Zhiqiang Xia; Zhengtao Wang; Yipeng Liu },
publisher = {IEEE SigPort},
title = {ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING},
year = {2017} }
TY - EJOUR
T1 - ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING
AU - Qi Guo; Ce Zhu; Zhiqiang Xia; Zhengtao Wang; Yipeng Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2146
ER -
Qi Guo, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, Yipeng Liu . (2017). ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING. IEEE SigPort. http://sigport.org/2146
Qi Guo, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, Yipeng Liu , 2017. ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING. Available at: http://sigport.org/2146.
Qi Guo, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, Yipeng Liu . (2017). "ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING." Web.
1. Qi Guo, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, Yipeng Liu . ATTRIBUTE-CONTROLLED FACE PHOTO SYNTHESIS FROM SIMPLE LINE DRAWING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2146

Search Video Action Proposal with Recurrent and Static YOLO


In this paper, we propose a new approach for searching action proposals in unconstrained videos. Our method first produces snippet action proposals by combining state-of-the-art YOLO detector (Static YOLO) and our regression based RNN detector (Recurrent YOLO). Then, these short action proposals are integrated to form final action proposals by solving two-pass dynamic programming which maximizes actioness score and temporal smoothness concurrently.

Paper Details

Authors:
Romain Vial, Hongyuan Zhu, Yonghong Tian, Shijian Lu
Submitted On:
20 September 2017 - 10:51am
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presentation.pdf

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[1] Romain Vial, Hongyuan Zhu, Yonghong Tian, Shijian Lu, "Search Video Action Proposal with Recurrent and Static YOLO", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2140. Accessed: Feb. 24, 2018.
@article{2140-17,
url = {http://sigport.org/2140},
author = {Romain Vial; Hongyuan Zhu; Yonghong Tian; Shijian Lu },
publisher = {IEEE SigPort},
title = {Search Video Action Proposal with Recurrent and Static YOLO},
year = {2017} }
TY - EJOUR
T1 - Search Video Action Proposal with Recurrent and Static YOLO
AU - Romain Vial; Hongyuan Zhu; Yonghong Tian; Shijian Lu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2140
ER -
Romain Vial, Hongyuan Zhu, Yonghong Tian, Shijian Lu. (2017). Search Video Action Proposal with Recurrent and Static YOLO. IEEE SigPort. http://sigport.org/2140
Romain Vial, Hongyuan Zhu, Yonghong Tian, Shijian Lu, 2017. Search Video Action Proposal with Recurrent and Static YOLO. Available at: http://sigport.org/2140.
Romain Vial, Hongyuan Zhu, Yonghong Tian, Shijian Lu. (2017). "Search Video Action Proposal with Recurrent and Static YOLO." Web.
1. Romain Vial, Hongyuan Zhu, Yonghong Tian, Shijian Lu. Search Video Action Proposal with Recurrent and Static YOLO [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2140

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