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Related Experiment Video

Updated: Sep 22, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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Effective Pre-Training Method and Its Compositional Intelligence for Image Captioning.

Won-Hyuk Choi1, Yong-Suk Choi1

  • 1Artificial Intelligence Laboratory, Hanyang University, Seoul 04763, Korea.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary
This summary is machine-generated.

Compositional Intelligence (CI) reduces deep learning training costs by reusing pre-trained models. Applying CI to image captioning significantly cut training time and improved performance, demonstrating its efficiency.

Keywords:
compositional intelligencefeature mapping layerimage captioningpre-training methodtransfer learningtransformer

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Deep Learning

Background:

  • Deep learning models are growing exponentially in parameter size, increasing computation and training costs.
  • High training costs hinder the development and deployment of advanced AI models.

Purpose of the Study:

  • To introduce Compositional Intelligence (CI), a novel method for reducing deep learning training costs.
  • To evaluate the effectiveness of CI in the image captioning task by combining pre-trained models.

Main Methods:

  • Proposed Compositional Intelligence (CI), a model reuse strategy combining pre-trained models.
  • Applied CI to the image captioning task, utilizing pre-trained feature extractors and caption generators (Transformer model).
  • Compared CI models against 'From Scratch' training using early stopping on the MS-COCO dataset.

Main Results:

  • The vanilla image captioning model using CI reduced training cost by 13.8% and improved performance by 3.2%.
  • The Object Relation Transformer model with CI achieved a 21.3% reduction in training cost.
  • CI demonstrated significant reductions in training cost and performance improvements.

Conclusions:

  • Compositional Intelligence (CI) offers an effective approach to mitigate the high training costs associated with large deep learning models.
  • Reusing pre-trained components via CI leads to substantial savings in computation and time while enhancing model performance in tasks like image captioning.