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A Review of Deep Learning for Video Captioning.

Moloud Abdar, Meenakshi Kollati, Swaraja Kuraparthi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 3, 2025
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    Summary
    This summary is machine-generated.

    This survey reviews deep learning methods for video captioning (VC), a technique that describes video content in natural language. It covers architectures, datasets, and future research directions for advancing VC applications.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Video captioning (VC) is a multidisciplinary research area.
    • VC aims to generate natural language descriptions for video content.
    • Applications include accessibility, video retrieval, and question answering.

    Purpose of the Study:

    • To provide a comprehensive review of deep learning-based video captioning methods.
    • To categorize and discuss various VC approaches.
    • To identify research gaps and future directions in the field.

    Main Methods:

    • Overview of problem formulation, evaluation metrics, and training losses.
    • Categorization of VC methods including attention-based architectures, graph networks, reinforcement learning, adversarial networks, and dense video captioning.
    • Review of existing datasets for video captioning.

    Main Results:

    • Detailed discussion of different deep learning-based VC architectures.
    • Analysis of current datasets and their suitability for VC tasks.
    • Identification of key research gaps and emerging trends.

    Conclusions:

    • Deep learning has significantly advanced video captioning capabilities.
    • Further research is needed in areas like complex scene understanding and long-form video description.
    • This survey serves as a guide for researchers in video captioning and related fields.