Multiple Hypothesis Video Relation Detection

Video relation in the form of triplet〈subject, predicate, object〉plays a vital role in video content understanding. Existing works on video relation detection are limited to associating short-term relations into long-term relations throughout the video, because of the inaccurate and missing problem...

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Bibliographic Details
Published in2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) pp. 287 - 291
Main Authors Di, Donglin, Shang, Xindi, Zhang, Weinan, Yang, Xun, Chua, Tat-Seng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
Feature extraction
Proposals
relational association
Robustness
Tagging
Task analysis
Trajectory
video relation detection
visual relationship
Visualization
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Summary:Video relation in the form of triplet〈subject, predicate, object〉plays a vital role in video content understanding. Existing works on video relation detection are limited to associating short-term relations into long-term relations throughout the video, because of the inaccurate and missing problem of short-term proposals. To alleviate the weakness of existing video relation detection methods, this work proposes a novel approach called Multi-Hypothesis Relational Association (MHRA), that can generate multiple hypotheses for video relation instances for more robust long-term relation prediction. Experiments on the benchmark dataset show that MHRA is able to outperform the state-of-the-art methods.
DOI:10.1109/BigMM.2019.000-9