research-article

Weakly Supervised Text-based Actor-Action Video Segmentation by Clip-level Multi-instance Learning

Authors:

Qingming HuangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 1

Article No.: 12, Pages 1 - 22

https://doi.org/10.1145/3514250

Published: 05 January 2023 Publication History

Abstract

In real-world scenarios, it is common that a video contains multiple actors and their activities. Selectively localizing one specific actor and its action spatially and temporally via a language query becomes a vital and challenging task. Existing fully supervised methods require extensive elaborately annotated data and are sensitive to the class labels, which cannot satisfy real-world applications’ needs. Thus, we introduce the task of weakly supervised actor-action video segmentation from a sentence query (AAVSS) in this work, where only the video-sentence pairs are provided. To the best of our knowledge, our work is the first to perform AAVSS under weakly supervised situations. However, this task is extremely challenging not only because the task aims to learn the complex interactions between two heterogeneous modalities but also because the task needs to learn fine-grained analysis of video content without pixel-level annotations. To overcome the challenges, we propose a two-stage network. The network first follows the sentence guidance to localize the candidate region and then performs segmentation to achieve selective segmentation. Specifically, a novel tracker-based clip-level multiple instance learning paradigm is proposed in this article to learn the matches between regions and sentences, which makes our two-stage network robust to the region proposal network. Furthermore, two intrinsic characteristics of the video, temporal consistency and motion information, are utilized in companion with the weak supervision to facilitate the region-query matching. Through extensive experiments, the proposed method achieves comparable performance to state-of-the-art fully supervised approaches on two large-scale benchmarks, including A2D Sentences and J-HMDB Sentences.

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Cited By

Zhou XYu HYang SHuo JTian P(2025)Learning from Orthogonal Space with Multimodal Large Models for Generalized Few-shot SegmentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3712597Online publication date: 20-Jan-2025
https://doi.org/10.1145/3712597
Jiang XYao YLiu SShen FNie LHua X(2024)Dual Dynamic Threshold Adjustment StrategyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365604720:7(1-18)Online publication date: 15-May-2024
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Marques GBoaro JBusson AGuedes ADuarte JColcher S(2024)Action Segmentation through Self-Supervised Video Features and Positional-Encoded EmbeddingsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364946520:9(1-23)Online publication date: 24-Feb-2024
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Index Terms

Weakly Supervised Text-based Actor-Action Video Segmentation by Clip-level Multi-instance Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 1

January 2023

505 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572858

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 05 January 2023

Online AM: 18 July 2022

Accepted: 28 January 2022

Revised: 25 January 2022

Received: 05 November 2021

Published in TOMM Volume 19, Issue 1

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Italy-China Collaboration Project TALENT
National Natural Science Foundation of China
Youth Innovation Promotion Association CAS
Fundamental Research Funds for Central Universities
China Postdoctoral Science Foundation Funded Project

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https://doi.org/10.1145/3712597
Jiang XYao YLiu SShen FNie LHua X(2024)Dual Dynamic Threshold Adjustment StrategyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365604720:7(1-18)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3656047
Marques GBoaro JBusson AGuedes ADuarte JColcher S(2024)Action Segmentation through Self-Supervised Video Features and Positional-Encoded EmbeddingsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364946520:9(1-23)Online publication date: 24-Feb-2024
https://dl.acm.org/doi/10.1145/3649465
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