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Multimodal Representation Learning via Maximization of Local Mutual Information.

Ruizhi Liao1, Daniel Moyer1, Miriam Cha2

  • 1CSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA.

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Summary

This study introduces a novel representation learning method that enhances image understanding by maximizing mutual information between image and text features. This approach improves image classification performance by leveraging descriptive text for richer image representations.

Keywords:
Local feature representationsMultimodal representation learningMutual information maximization

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Image representations are crucial for various downstream tasks.
  • Leveraging descriptive text can enrich image understanding.
  • Existing methods may not fully exploit the local correlations between image and text features.

Purpose of the Study:

  • To propose and demonstrate a representation learning approach using mutual information.
  • To learn useful image representations by integrating information from descriptive text.
  • To improve the quality of image representations for downstream applications.

Main Methods:

  • Developed a method to maximize mutual information between local image and text features.
  • Employed neural network discriminators for mutual information estimation.
  • Trained image and text encoders to produce representations with high local mutual information.

Main Results:

  • Experimental results show advantages in downstream image classification tasks.
  • The proposed method effectively learns from the synergy between local image and text features.
  • Demonstrated the efficacy of maximizing local mutual information for representation learning.

Conclusions:

  • Maximizing local mutual information is a viable strategy for representation learning.
  • Integrating image and text data through mutual information enhances image representations.
  • The approach shows promise for improving performance in image-related AI tasks.