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Related Experiment Video

Updated: Jul 1, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Image Caption Generation using Deep Learning Approaches.

Arun Pratap Singh1, Manish Manoria2, Sunil Joshi3

  • 1Samrat Ashok Technological Institute; arun20cspd03@satiengg.in.

Journal of Visualized Experiments : Jove
|June 29, 2026
PubMed
Summary

This study enhances image caption generation using Residual Network (ResNet) and smart filters for deeper image classification. The approach achieves precise, meaningful descriptions by combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Image caption generation aims to create textual descriptions for images.
  • Residual Network (ResNet) excels in image classification due to its deep hierarchical representations and residual connections that mitigate vanishing gradients.
  • Existing methods require enhancement for generating highly precise and meaningful image descriptions.

Purpose of the Study:

  • To improve image caption generation by employing ResNet with smart filters for deeper image classification.
  • To generate genuine and meaningful descriptions that are highly precise relative to reference captions.
  • To leverage ResNet's generalization capabilities for diverse image datasets.

Main Methods:

  • Image enhancement using smart filtering techniques.

Related Experiment Videos

Last Updated: Jul 1, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

  • Feature encoding via Convolutional Neural Networks (CNNs).
  • Model training and feature decoding using Recurrent Neural Networks (RNNs) on the MSCOCO benchmark dataset.

    • Main Results:

    • Achieved BLUE scores: B1: 0.579, B2: 0.404, B3: 0.279, B4: 0.191.
    • Achieved METEOR score: 0.195.
    • Achieved ROUGE score: 0.396 and CIDEr score: 0.6.

    Conclusions:

    • The integration of ResNet with smart filters significantly enhances the precision and meaningfulness of generated image captions.
    • The proposed method demonstrates strong performance on the MSCOCO dataset, indicating effective generalization.
    • This approach offers a robust solution for advanced image captioning tasks in computer vision.