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Related Experiment Video

Updated: Jan 8, 2026

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
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Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

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Weakly and Single-Frame Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal Learning.

Minghang Zheng, Yanjie Huang, Qingchao Chen

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 17, 2025
    PubMed
    Summary

    Contrastive Proposal Learning (CPL) improves weakly-supervised temporal sentence grounding by generating better proposals within videos. This method effectively distinguishes confusing scenes and balances annotation costs for state-of-the-art performance.

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
    05:54

    Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

    Published on: October 18, 2018

    6.6K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Temporal sentence grounding localizes video moments based on language queries.
    • Weakly-supervised methods are gaining traction due to the difficulty of temporal boundary annotation.
    • Existing methods use low-quality, content-independent proposals and ignore in-video negative samples.

    Purpose of the Study:

    • To introduce Contrastive Proposal Learning (CPL) for improved weakly-supervised temporal sentence grounding.
    • To address limitations of existing methods, including proposal quality and negative sample selection.
    • To explore a balance between annotation effort and grounding performance.

    Main Methods:

    • Generating positive and negative proposals within the same video using learnable asymmetric Gaussian functions.
    • Implementing a controllable, easy-to-hard negative proposal mining strategy to identify confusing segments.
    • Extending proposal generation for low-cost single-frame annotation.

    Main Results:

    • CPL effectively distinguishes highly confusing video segments.
    • The method achieves state-of-the-art performance on benchmark datasets (Charades-STA, ActivityNet Captions, DiDeMo).
    • The approach demonstrates applicability to both MIL-based and reconstruction-based frameworks.

    Conclusions:

    • Contrastive Proposal Learning offers a superior approach to weakly-supervised temporal sentence grounding.
    • The proposed methods enhance proposal generation and negative sampling strategies.
    • CPL provides a flexible solution adaptable to different annotation burdens and achieves top-tier results.