abstract

Trustworthy Multimedia Analysis

Authors:

Jitao SangAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 5667 - 5669

https://doi.org/10.1145/3474085.3478871

Published: 17 October 2021 Publication History

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Abstract

This tutorial discusses the trustworthiness issue in multimedia analysis. Starting from introducing two types of spurious correlations learned from distilling human knowledge, we partition the (visual) feature space along two dimensions of task-relevance and semantic-orientation. Trustworthy multimedia analysis ideally relies on the task-relevant semantic features and consists of three modules as trainer, interpreter and tester. These three modules essentially form a closed loop, which respectively address goals of extracting task-relevant features, extracting task-relevant semantic features, and detecting spurious correlations to be corrected by the trainer and interpreter.

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Cited By

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Zhang YWang JSang JMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Counterfactually Measuring and Eliminating Social Bias in Vision-Language Pre-training ModelsProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548396(4996-5004)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548396

Index Terms

Trustworthy Multimedia Analysis
1. Computing methodologies
  1. Artificial intelligence

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Information

Published In

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2021

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Funding Sources

the Fundamental Research Funds for the Central Universities
the Fundamental Research Funds for the Central Universities
Beijing Natural Science Foundation

Conference

MM '21

Sponsor:

SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

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

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Cited By

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Zhang YWang JSang JMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Counterfactually Measuring and Eliminating Social Bias in Vision-Language Pre-training ModelsProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548396(4996-5004)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548396

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