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Parameter Adaptive Contrastive Hashing for multimedia retrieval.

Yunfei Chen1, Yitian Long2, Zhan Yang1

  • 1Big Data Institute, School of Computer Science and Engineering, Central South University, ChangSha, Hunan, 410000, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Parameter Adaptive Contrastive Hashing (PACH) for efficient multimedia retrieval. PACH overcomes hyperparameter tuning challenges in unsupervised hash learning, improving semantic similarity and retrieval accuracy.

Keywords:
Fast Parameter AdaptiveMulti-source heterogeneousMultimedia Contrastive HashingMultimedia retrieval

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Machine Learning

Background:

  • The proliferation of internet data necessitates efficient information retrieval methods.
  • Hash learning is a mainstream technique for multimedia retrieval due to its speed and efficiency.
  • Unsupervised multimedia hash learning methods struggle with hyperparameter tuning and semantic similarity guidance.

Purpose of the Study:

  • To propose a novel Parameter Adaptive Contrastive Hashing (PACH) method for multimedia retrieval.
  • To address the limitations of existing unsupervised hash learning techniques.
  • To enhance the accuracy and efficiency of multimedia information retrieval.

Main Methods:

  • Developed a Fast Parameter Adaptive (FPA) module using reinforcement learning for automatic hyperparameter optimization.
  • Integrated the FPA module with a Multimedia Contrastive Hashing (MCH) module to explore semantic consistency across modalities.
  • Designed comprehensive experiments to evaluate the proposed PACH method against state-of-the-art techniques.

Main Results:

  • The PACH method demonstrated superior performance in multimedia retrieval tasks.
  • The FPA module effectively identified optimal parameter combinations, simplifying the tuning process.
  • The MCH module successfully enriched hash codes with cross-modal semantic information.

Conclusions:

  • The proposed PACH method offers an effective solution for multimedia retrieval challenges.
  • PACH enhances retrieval accuracy by addressing hyperparameter tuning and semantic similarity issues.
  • The method provides a robust and efficient approach to unsupervised multimedia hash learning.