New feature-level video classification via temporal attention model

Hongje Seong; Suhan Woo; Junhyuk Hyun; Hyunbae Chang; Suhyeon Lee; Euntai Kim

doi:10.1145/3265987.3265990

New feature-level video classification via temporal attention model

Hongje Seong, Suhan Woo, Junhyuk Hyun, Hyunbae Chang, Suhyeon Lee, Euntai Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

CoVieW 2018 is a new challenge which aims at simultaneous scene and action recognition for untrimmed video [1]. In the challenge, frame-level video features extracted by pre-trained deep convolutional neural network (CNN) are provided for video-level classification. In this paper, a new approach for the video-level classification method is proposed. The proposed method focuses on the analysis in temporal domain and the temporal attention model is developed. To compensate for the differences in the lengths of various videos, temporal padding method is also developed to unify the lengths of videos. Further, data augmentation is performed to enhance some validation accuracy. Finally, for the train/validation in CoView 2018 dataset we recorded the performance of 95.53% accuracy in the scene and 87.17% accuracy in the action using temporal attention model, nonzero padding and data augmentation. The top-1 hamming score is the standard metric in the CoVieW 2018 challenge and 91.35% is obtained by the proposed method.

Original language	English
Title of host publication	CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018
Publisher	Association for Computing Machinery, Inc
Pages	31-34
Number of pages	4
ISBN (Electronic)	9781450359764
DOIs	https://doi.org/10.1145/3265987.3265990
Publication status	Published - 2018 Oct 15
Event	1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, CoVieW 2018, in conjunction with ACM Multimedia, MM 2018 - Seoul, Korea, Republic of Duration: 2018 Oct 22 → …

Publication series

Name	CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018

Other

Other	1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, CoVieW 2018, in conjunction with ACM Multimedia, MM 2018
Country/Territory	Korea, Republic of
City	Seoul
Period	18/10/22 → …

Bibliographical note

Funding Information:
This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7069370).

Publisher Copyright:
© 2018 Association for Computing Machinery.

All Science Journal Classification (ASJC) codes

Computer Science(all)
Health Informatics
Media Technology

Access to Document

10.1145/3265987.3265990

Cite this

Seong, H., Woo, S., Hyun, J., Chang, H., Lee, S., & Kim, E. (2018). New feature-level video classification via temporal attention model. In CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018 (pp. 31-34). (CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018). Association for Computing Machinery, Inc. https://doi.org/10.1145/3265987.3265990

Seong, Hongje ; Woo, Suhan ; Hyun, Junhyuk et al. / New feature-level video classification via temporal attention model. CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018. Association for Computing Machinery, Inc, 2018. pp. 31-34 (CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018).

@inproceedings{072b165963ae48c1b9128b124d35dc09,

title = "New feature-level video classification via temporal attention model",

abstract = "CoVieW 2018 is a new challenge which aims at simultaneous scene and action recognition for untrimmed video [1]. In the challenge, frame-level video features extracted by pre-trained deep convolutional neural network (CNN) are provided for video-level classification. In this paper, a new approach for the video-level classification method is proposed. The proposed method focuses on the analysis in temporal domain and the temporal attention model is developed. To compensate for the differences in the lengths of various videos, temporal padding method is also developed to unify the lengths of videos. Further, data augmentation is performed to enhance some validation accuracy. Finally, for the train/validation in CoView 2018 dataset we recorded the performance of 95.53% accuracy in the scene and 87.17% accuracy in the action using temporal attention model, nonzero padding and data augmentation. The top-1 hamming score is the standard metric in the CoVieW 2018 challenge and 91.35% is obtained by the proposed method.",

author = "Hongje Seong and Suhan Woo and Junhyuk Hyun and Hyunbae Chang and Suhyeon Lee and Euntai Kim",

note = "Funding Information: This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7069370). Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, CoVieW 2018, in conjunction with ACM Multimedia, MM 2018 ; Conference date: 22-10-2018",

year = "2018",

month = oct,

day = "15",

doi = "10.1145/3265987.3265990",

language = "English",

series = "CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018",

publisher = "Association for Computing Machinery, Inc",

pages = "31--34",

booktitle = "CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018",

}

Seong, H, Woo, S, Hyun, J, Chang, H, Lee, S & Kim, E 2018, New feature-level video classification via temporal attention model. in CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018. CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018, Association for Computing Machinery, Inc, pp. 31-34, 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, CoVieW 2018, in conjunction with ACM Multimedia, MM 2018, Seoul, Korea, Republic of, 18/10/22. https://doi.org/10.1145/3265987.3265990

New feature-level video classification via temporal attention model. / Seong, Hongje; Woo, Suhan; Hyun, Junhyuk et al.
CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018. Association for Computing Machinery, Inc, 2018. p. 31-34 (CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - New feature-level video classification via temporal attention model

AU - Seong, Hongje

AU - Woo, Suhan

AU - Hyun, Junhyuk

AU - Chang, Hyunbae

AU - Lee, Suhyeon

AU - Kim, Euntai

N1 - Funding Information: This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7069370). Publisher Copyright: © 2018 Association for Computing Machinery.

PY - 2018/10/15

Y1 - 2018/10/15

N2 - CoVieW 2018 is a new challenge which aims at simultaneous scene and action recognition for untrimmed video [1]. In the challenge, frame-level video features extracted by pre-trained deep convolutional neural network (CNN) are provided for video-level classification. In this paper, a new approach for the video-level classification method is proposed. The proposed method focuses on the analysis in temporal domain and the temporal attention model is developed. To compensate for the differences in the lengths of various videos, temporal padding method is also developed to unify the lengths of videos. Further, data augmentation is performed to enhance some validation accuracy. Finally, for the train/validation in CoView 2018 dataset we recorded the performance of 95.53% accuracy in the scene and 87.17% accuracy in the action using temporal attention model, nonzero padding and data augmentation. The top-1 hamming score is the standard metric in the CoVieW 2018 challenge and 91.35% is obtained by the proposed method.

AB - CoVieW 2018 is a new challenge which aims at simultaneous scene and action recognition for untrimmed video [1]. In the challenge, frame-level video features extracted by pre-trained deep convolutional neural network (CNN) are provided for video-level classification. In this paper, a new approach for the video-level classification method is proposed. The proposed method focuses on the analysis in temporal domain and the temporal attention model is developed. To compensate for the differences in the lengths of various videos, temporal padding method is also developed to unify the lengths of videos. Further, data augmentation is performed to enhance some validation accuracy. Finally, for the train/validation in CoView 2018 dataset we recorded the performance of 95.53% accuracy in the scene and 87.17% accuracy in the action using temporal attention model, nonzero padding and data augmentation. The top-1 hamming score is the standard metric in the CoVieW 2018 challenge and 91.35% is obtained by the proposed method.

UR - http://www.scopus.com/inward/record.url?scp=85058182380&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058182380&partnerID=8YFLogxK

U2 - 10.1145/3265987.3265990

DO - 10.1145/3265987.3265990

M3 - Conference contribution

AN - SCOPUS:85058182380

T3 - CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018

SP - 31

EP - 34

BT - CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018

PB - Association for Computing Machinery, Inc

T2 - 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, CoVieW 2018, in conjunction with ACM Multimedia, MM 2018

Y2 - 22 October 2018

ER -

Seong H, Woo S, Hyun J, Chang H, Lee S, Kim E. New feature-level video classification via temporal attention model. In CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018. Association for Computing Machinery, Inc. 2018. p. 31-34. (CoVieW 2018 - Proceedings of the 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, co-located with MM 2018). doi: 10.1145/3265987.3265990

New feature-level video classification via temporal attention model

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this