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Image/Video Storage, Retrieval

SOCIAL RELATION RECOGNITION IN EGOCENTRIC PHOTOSTREAMS


This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental’s social theory, that groups human relations into five social domains with related categories.

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Authors:
Petia Radeva, Mariella Dimiccoli
Submitted On:
24 September 2019 - 4:30am
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ICIP 2019 Presentation.pdf

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[1] Petia Radeva, Mariella Dimiccoli, "SOCIAL RELATION RECOGNITION IN EGOCENTRIC PHOTOSTREAMS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4781. Accessed: Sep. 20, 2020.
@article{4781-19,
url = {http://sigport.org/4781},
author = {Petia Radeva; Mariella Dimiccoli },
publisher = {IEEE SigPort},
title = {SOCIAL RELATION RECOGNITION IN EGOCENTRIC PHOTOSTREAMS},
year = {2019} }
TY - EJOUR
T1 - SOCIAL RELATION RECOGNITION IN EGOCENTRIC PHOTOSTREAMS
AU - Petia Radeva; Mariella Dimiccoli
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4781
ER -
Petia Radeva, Mariella Dimiccoli. (2019). SOCIAL RELATION RECOGNITION IN EGOCENTRIC PHOTOSTREAMS. IEEE SigPort. http://sigport.org/4781
Petia Radeva, Mariella Dimiccoli, 2019. SOCIAL RELATION RECOGNITION IN EGOCENTRIC PHOTOSTREAMS. Available at: http://sigport.org/4781.
Petia Radeva, Mariella Dimiccoli. (2019). "SOCIAL RELATION RECOGNITION IN EGOCENTRIC PHOTOSTREAMS." Web.
1. Petia Radeva, Mariella Dimiccoli. SOCIAL RELATION RECOGNITION IN EGOCENTRIC PHOTOSTREAMS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4781

TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FOR AUTONOMOUS VEHICLES


Vehicle localization is a critical component for autonomous driving, which estimates the position and orientation of vehicles. To achieve the goal of quick and accurate localization, we develop a system that can dynamically switch the features applied for localization. Specifically, we develop a feature based on convolutional neural network targeting at accurate matching, which proves high rotation invariant property that can help to overcome the relatively large error when vehicles turning at corners.

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Authors:
Guoyu Lu, Xue-iuan Wong
Submitted On:
20 September 2019 - 5:45am
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ICIP_poster_3818.pdf

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[1] Guoyu Lu, Xue-iuan Wong, "TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FOR AUTONOMOUS VEHICLES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4773. Accessed: Sep. 20, 2020.
@article{4773-19,
url = {http://sigport.org/4773},
author = {Guoyu Lu; Xue-iuan Wong },
publisher = {IEEE SigPort},
title = {TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FOR AUTONOMOUS VEHICLES},
year = {2019} }
TY - EJOUR
T1 - TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FOR AUTONOMOUS VEHICLES
AU - Guoyu Lu; Xue-iuan Wong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4773
ER -
Guoyu Lu, Xue-iuan Wong. (2019). TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FOR AUTONOMOUS VEHICLES. IEEE SigPort. http://sigport.org/4773
Guoyu Lu, Xue-iuan Wong, 2019. TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FOR AUTONOMOUS VEHICLES. Available at: http://sigport.org/4773.
Guoyu Lu, Xue-iuan Wong. (2019). "TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FOR AUTONOMOUS VEHICLES." Web.
1. Guoyu Lu, Xue-iuan Wong. TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FOR AUTONOMOUS VEHICLES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4773

Efficient Codebook and Factorization for Second-Order Representation Learning

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19 September 2019 - 1:41pm
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ICIP 2019 - JCF(3).pdf

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[1] , "Efficient Codebook and Factorization for Second-Order Representation Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4746. Accessed: Sep. 20, 2020.
@article{4746-19,
url = {http://sigport.org/4746},
author = { },
publisher = {IEEE SigPort},
title = {Efficient Codebook and Factorization for Second-Order Representation Learning},
year = {2019} }
TY - EJOUR
T1 - Efficient Codebook and Factorization for Second-Order Representation Learning
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4746
ER -
. (2019). Efficient Codebook and Factorization for Second-Order Representation Learning. IEEE SigPort. http://sigport.org/4746
, 2019. Efficient Codebook and Factorization for Second-Order Representation Learning. Available at: http://sigport.org/4746.
. (2019). "Efficient Codebook and Factorization for Second-Order Representation Learning." Web.
1. . Efficient Codebook and Factorization for Second-Order Representation Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4746

UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION BASED ON ATTRIBUTES


Pedestrian attributes, e.g., hair length, clothes type and color, locally describe the semantic appearance of a person. Training person re-identification (ReID) algorithms under the supervision of such attributes have proven to be effective in extracting local features. Different from person identity, at- tributes are consistent across different domains (or datasets). However, most of ReID datasets lack attribute annotations. On the other hand, there are several datasets labeled with sufficient attributes for the case of pedestrian attribute recognition.

Paper Details

Authors:
Xiangping Zhu, Pietro Morerio and Vittorio Murino
Submitted On:
16 September 2019 - 10:17am
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#2281_poster

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[1] Xiangping Zhu, Pietro Morerio and Vittorio Murino, "UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION BASED ON ATTRIBUTES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4640. Accessed: Sep. 20, 2020.
@article{4640-19,
url = {http://sigport.org/4640},
author = {Xiangping Zhu; Pietro Morerio and Vittorio Murino },
publisher = {IEEE SigPort},
title = {UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION BASED ON ATTRIBUTES},
year = {2019} }
TY - EJOUR
T1 - UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION BASED ON ATTRIBUTES
AU - Xiangping Zhu; Pietro Morerio and Vittorio Murino
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4640
ER -
Xiangping Zhu, Pietro Morerio and Vittorio Murino. (2019). UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION BASED ON ATTRIBUTES. IEEE SigPort. http://sigport.org/4640
Xiangping Zhu, Pietro Morerio and Vittorio Murino, 2019. UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION BASED ON ATTRIBUTES. Available at: http://sigport.org/4640.
Xiangping Zhu, Pietro Morerio and Vittorio Murino. (2019). "UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION BASED ON ATTRIBUTES." Web.
1. Xiangping Zhu, Pietro Morerio and Vittorio Murino. UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION BASED ON ATTRIBUTES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4640

Augmented Visual-semantic Embeddings for Image and Sentence Matching

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16 September 2019 - 4:23am
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icip.pdf

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[1] , "Augmented Visual-semantic Embeddings for Image and Sentence Matching", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4635. Accessed: Sep. 20, 2020.
@article{4635-19,
url = {http://sigport.org/4635},
author = { },
publisher = {IEEE SigPort},
title = {Augmented Visual-semantic Embeddings for Image and Sentence Matching},
year = {2019} }
TY - EJOUR
T1 - Augmented Visual-semantic Embeddings for Image and Sentence Matching
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4635
ER -
. (2019). Augmented Visual-semantic Embeddings for Image and Sentence Matching. IEEE SigPort. http://sigport.org/4635
, 2019. Augmented Visual-semantic Embeddings for Image and Sentence Matching. Available at: http://sigport.org/4635.
. (2019). "Augmented Visual-semantic Embeddings for Image and Sentence Matching." Web.
1. . Augmented Visual-semantic Embeddings for Image and Sentence Matching [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4635

Loss Switching Fusion with Similarity Search for Video Classification


From video streaming to security and surveillance applications , video data play an important role in our daily living today. However, managing a large amount of video data and retrieving the most useful information for the user remain a challenging task. In this paper, we propose a novel video classification system that would benefit the scene understanding task. We define our classification problem as classifying background and foreground motions using the same feature representation for outdoor scenes.

Paper Details

Authors:
Lei Wang, Du Q. Huynh, Moussa Reda Mansour
Submitted On:
16 September 2019 - 1:04am
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eposter icip2019 leiwang

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[1] Lei Wang, Du Q. Huynh, Moussa Reda Mansour, "Loss Switching Fusion with Similarity Search for Video Classification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4630. Accessed: Sep. 20, 2020.
@article{4630-19,
url = {http://sigport.org/4630},
author = {Lei Wang; Du Q. Huynh; Moussa Reda Mansour },
publisher = {IEEE SigPort},
title = {Loss Switching Fusion with Similarity Search for Video Classification},
year = {2019} }
TY - EJOUR
T1 - Loss Switching Fusion with Similarity Search for Video Classification
AU - Lei Wang; Du Q. Huynh; Moussa Reda Mansour
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4630
ER -
Lei Wang, Du Q. Huynh, Moussa Reda Mansour. (2019). Loss Switching Fusion with Similarity Search for Video Classification. IEEE SigPort. http://sigport.org/4630
Lei Wang, Du Q. Huynh, Moussa Reda Mansour, 2019. Loss Switching Fusion with Similarity Search for Video Classification. Available at: http://sigport.org/4630.
Lei Wang, Du Q. Huynh, Moussa Reda Mansour. (2019). "Loss Switching Fusion with Similarity Search for Video Classification." Web.
1. Lei Wang, Du Q. Huynh, Moussa Reda Mansour. Loss Switching Fusion with Similarity Search for Video Classification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4630

poster of "joint image restoration and matching based on hierarchical sparse representation"

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14 September 2019 - 4:30am
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ICIP2019_POSTER.pdf

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[1] , "poster of "joint image restoration and matching based on hierarchical sparse representation"", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4615. Accessed: Sep. 20, 2020.
@article{4615-19,
url = {http://sigport.org/4615},
author = { },
publisher = {IEEE SigPort},
title = {poster of "joint image restoration and matching based on hierarchical sparse representation"},
year = {2019} }
TY - EJOUR
T1 - poster of "joint image restoration and matching based on hierarchical sparse representation"
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4615
ER -
. (2019). poster of "joint image restoration and matching based on hierarchical sparse representation". IEEE SigPort. http://sigport.org/4615
, 2019. poster of "joint image restoration and matching based on hierarchical sparse representation". Available at: http://sigport.org/4615.
. (2019). "poster of "joint image restoration and matching based on hierarchical sparse representation"." Web.
1. . poster of "joint image restoration and matching based on hierarchical sparse representation" [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4615

Learning Search Path for Region-Level Image Matching

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Authors:
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino
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20 May 2019 - 10:45pm
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icassp_19_poster_onkar.pdf

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[1] Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino , "Learning Search Path for Region-Level Image Matching", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4555. Accessed: Sep. 20, 2020.
@article{4555-19,
url = {http://sigport.org/4555},
author = {Onkar Krishna; Go Irie; Xiaomeng Wu; Takahito Kawanishi; Kunio Kashino },
publisher = {IEEE SigPort},
title = {Learning Search Path for Region-Level Image Matching},
year = {2019} }
TY - EJOUR
T1 - Learning Search Path for Region-Level Image Matching
AU - Onkar Krishna; Go Irie; Xiaomeng Wu; Takahito Kawanishi; Kunio Kashino
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4555
ER -
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . (2019). Learning Search Path for Region-Level Image Matching. IEEE SigPort. http://sigport.org/4555
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino , 2019. Learning Search Path for Region-Level Image Matching. Available at: http://sigport.org/4555.
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . (2019). "Learning Search Path for Region-Level Image Matching." Web.
1. Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . Learning Search Path for Region-Level Image Matching [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4555

Learning Search Path for Region-Level Image Matching

Paper Details

Authors:
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino
Submitted On:
20 May 2019 - 10:45pm
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ICASSP_19 poster

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[1] Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino , "Learning Search Path for Region-Level Image Matching", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4554. Accessed: Sep. 20, 2020.
@article{4554-19,
url = {http://sigport.org/4554},
author = {Onkar Krishna; Go Irie; Xiaomeng Wu; Takahito Kawanishi; Kunio Kashino },
publisher = {IEEE SigPort},
title = {Learning Search Path for Region-Level Image Matching},
year = {2019} }
TY - EJOUR
T1 - Learning Search Path for Region-Level Image Matching
AU - Onkar Krishna; Go Irie; Xiaomeng Wu; Takahito Kawanishi; Kunio Kashino
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4554
ER -
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . (2019). Learning Search Path for Region-Level Image Matching. IEEE SigPort. http://sigport.org/4554
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino , 2019. Learning Search Path for Region-Level Image Matching. Available at: http://sigport.org/4554.
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . (2019). "Learning Search Path for Region-Level Image Matching." Web.
1. Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . Learning Search Path for Region-Level Image Matching [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4554

Gradient Image Super-Resolution for Low-Resolution Image Recognition


In visual object recognition problems essential to surveillance and navigation problems in a variety of military and civilian use cases,low-resolution and low-quality images present great challenges to this problem. Recent advancements in deep learning based methods like EDSR/VDSR have boosted pixel domain image super-resolution(SR) performances significantly in terms of signal to noise ratio(SNR)/mean square error(MSE) metrics of the super-resolved image.

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Authors:
Dewan Fahim Noor, Yue Li, Zhu Li, Shuvra Bhattacharyya, George York
Submitted On:
10 May 2019 - 3:40pm
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Gradient_image_SR_icassp.pdf

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[1] Dewan Fahim Noor, Yue Li, Zhu Li, Shuvra Bhattacharyya, George York, "Gradient Image Super-Resolution for Low-Resolution Image Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4395. Accessed: Sep. 20, 2020.
@article{4395-19,
url = {http://sigport.org/4395},
author = {Dewan Fahim Noor; Yue Li; Zhu Li; Shuvra Bhattacharyya; George York },
publisher = {IEEE SigPort},
title = {Gradient Image Super-Resolution for Low-Resolution Image Recognition},
year = {2019} }
TY - EJOUR
T1 - Gradient Image Super-Resolution for Low-Resolution Image Recognition
AU - Dewan Fahim Noor; Yue Li; Zhu Li; Shuvra Bhattacharyya; George York
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4395
ER -
Dewan Fahim Noor, Yue Li, Zhu Li, Shuvra Bhattacharyya, George York. (2019). Gradient Image Super-Resolution for Low-Resolution Image Recognition. IEEE SigPort. http://sigport.org/4395
Dewan Fahim Noor, Yue Li, Zhu Li, Shuvra Bhattacharyya, George York, 2019. Gradient Image Super-Resolution for Low-Resolution Image Recognition. Available at: http://sigport.org/4395.
Dewan Fahim Noor, Yue Li, Zhu Li, Shuvra Bhattacharyya, George York. (2019). "Gradient Image Super-Resolution for Low-Resolution Image Recognition." Web.
1. Dewan Fahim Noor, Yue Li, Zhu Li, Shuvra Bhattacharyya, George York. Gradient Image Super-Resolution for Low-Resolution Image Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4395

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