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Evaluation of automatic video captioning using direct assessment.

Yvette Graham1, George Awad2,3, Alan Smeaton4

  • 1ADAPT Centre for Digital Content Technology, Dublin City University, Glasnevin, Dublin 9, Ireland.

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Summary
This summary is machine-generated.

Direct Assessment, a novel human-based method, evaluates automatic video caption quality. This approach overcomes limitations of existing metrics, offering a robust and scalable solution for assessing video captioning techniques.

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Human-Computer Interaction

Background:

  • Evaluating automatic video caption quality is challenging due to the lack of definitive ground truth.
  • Existing metrics like BLEU and METEOR have demonstrated weaknesses in accurately assessing caption performance.
  • Previous methods relied on automated comparisons, failing to capture nuanced descriptive accuracy.

Purpose of the Study:

  • To introduce Direct Assessment, a crowdsourced manual evaluation method for video caption quality.
  • To address the limitations of automated metrics in video caption evaluation.
  • To provide a robust and scalable framework for assessing diverse caption-generation techniques.

Main Methods:

  • Crowdsourcing manual assessments of automatically generated video captions.
  • Implementing a quality rating system for human assessors.
  • Automatically degrading caption quality to calibrate human judgments.
  • Utilizing data from the TRECVid video-to-text tasks (2016 and 2017).

Main Results:

  • Demonstrated the replicability and robustness of the Direct Assessment method.
  • Showcased the scalability of the approach for evaluating multiple captioning techniques.
  • Established a reliable way to incorporate human judgment into caption quality evaluation.

Conclusions:

  • Direct Assessment offers a superior and more reliable method for evaluating automatic video captions compared to traditional metrics.
  • The method effectively integrates human perception to assess how well captions describe video content.
  • This approach is suitable for large-scale evaluations and future advancements in video captioning technology.