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ResidualDenseNetwork: A Simple Approach for Video Person Identification

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Jianxin LiaoAuthors Info & Claims

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

Pages 2521 - 2525

https://doi.org/10.1145/3343031.3356050

Published: 15 October 2019 Publication History

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Abstract

Video identification is an important task in the practical application and industry. Based on the iQIYI-VID-2019 dataset, ACM International Conference on Multimedia and iQIYI co-hosted the celebrity video identification challenge. We take part in the competition, propose a new feature fusion method and design a residual dense network which can improve video identification performance in the complex scenes. Only with face features, we achieve 0.9035 in mean Average Precision(mAP) which win the second place on the leadboard. At the same time, it is the best score only with official features. It is worth mention that the flops of our model is only 0.5G and the time required to predict the entire test dataset is only 2 sim 5 minutes. Our method takes accuracy and speed into account, which has a strong practical significance.

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Cited By

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Xie QLu ZZhou WLi H(2023)Improving Person Re-Identification With Multi-Cue Similarity Embedding and PropagationIEEE Transactions on Multimedia10.1109/TMM.2022.320794925(6384-6396)Online publication date: 2023
https://doi.org/10.1109/TMM.2022.3207949
Liao ZDi DHao JZhang JZhu SYin J(2023)MMM-GCN: Multi-Level Multi-Modal Graph Convolution Network for Video-Based Person IdentificationMultiMedia Modeling10.1007/978-3-031-27077-2_1(3-15)Online publication date: 29-Mar-2023
https://doi.org/10.1007/978-3-031-27077-2_1
Wang WWu BLi FLiu Z(2020)Multi-Cue and Temporal Attention for Person Recognition in VideosPattern Recognition and Computer Vision10.1007/978-3-030-60639-8_31(369-380)Online publication date: 15-Oct-2020
https://doi.org/10.1007/978-3-030-60639-8_31

Index Terms

ResidualDenseNetwork: A Simple Approach for Video Person Identification
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Video search

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Information & Contributors

Information

Published In

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

October 2019

2794 pages

ISBN:9781450368896

DOI:10.1145/3343031

General Chairs:
Laurent Amsaleg
CNRS-IRISA, France
,
Benoit Huet
EURECOM, France
,
Martha Larson
Radboud University and TU Delft (Netherlands)
,
Program Chairs:
Guillaume Gravier
CNRS-IRISA, France
,
Hayley Hung
Delft University of Technology Netherlands
,
Chong-Wah Ngo
City University of Hong Kong Hong Kong
,
Wei Tsang Ooi
National University of Singapore Singapore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2019

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Funding Sources

Beijing Municipal Natural Science Foundation
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China

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MM '19

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SIGMM

MM '19: The 27th ACM International Conference on Multimedia

October 21 - 25, 2019

Nice, France

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MM '19 Paper Acceptance Rate 252 of 936 submissions, 27%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all

Xie QLu ZZhou WLi H(2023)Improving Person Re-Identification With Multi-Cue Similarity Embedding and PropagationIEEE Transactions on Multimedia10.1109/TMM.2022.320794925(6384-6396)Online publication date: 2023
https://doi.org/10.1109/TMM.2022.3207949
Liao ZDi DHao JZhang JZhu SYin J(2023)MMM-GCN: Multi-Level Multi-Modal Graph Convolution Network for Video-Based Person IdentificationMultiMedia Modeling10.1007/978-3-031-27077-2_1(3-15)Online publication date: 29-Mar-2023
https://doi.org/10.1007/978-3-031-27077-2_1
Wang WWu BLi FLiu Z(2020)Multi-Cue and Temporal Attention for Person Recognition in VideosPattern Recognition and Computer Vision10.1007/978-3-030-60639-8_31(369-380)Online publication date: 15-Oct-2020
https://doi.org/10.1007/978-3-030-60639-8_31

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iQIYI Celebrity Video Identification Challenge

A Novel Deep Multi-Modal Feature Fusion Method for Celebrity Video Identification

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