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Related Experiment Video

Updated: Oct 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Deep Unsupervised Hashing for Large-Scale Cross-Modal Retrieval Using Knowledge Distillation Model.

Mingyong Li¹, Qiqi Li¹, Lirong Tang¹

¹College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China.

Computational Intelligence and Neuroscience

|July 30, 2021

Summary

This summary is machine-generated.

This study introduces a new unsupervised knowledge distillation method for cross-modal hashing, improving retrieval accuracy without manual data labeling. The Semantic Alignment Knowledge Distillation Hashing (SAKDH) method enhances search performance on large datasets.

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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Cross-modal hashing enables efficient retrieval across diverse data types but supervised methods require extensive manual annotation.
Unsupervised deep hashing struggles with performance due to a lack of supervisory signals.

Purpose of the Study:

To develop a novel unsupervised knowledge distillation cross-modal hashing method (SAKDH) to overcome limitations of existing approaches.
To leverage semantic alignment and knowledge distillation for improved unsupervised cross-modal hashing.

Main Methods:

A teacher-student model framework is employed, inspired by knowledge distillation.
The unsupervised teacher model uses semantic alignment to construct a modal fusion similarity matrix.
The student model is guided by the teacher's distilled information to generate discriminative hash codes.

Main Results:

The proposed SAKDH method significantly improved Mean Average Precision (MAP) compared to representative unsupervised methods.
Experiments were conducted on MIRFLICKR-25K and NUS-WIDE benchmark datasets.
The method demonstrated effectiveness in large-scale cross-modal data retrieval.

Conclusions:

SAKDH effectively addresses the challenge of unsupervised cross-modal hashing by reconstructing similarity matrices.
The approach enhances the accuracy and efficiency of cross-modal retrieval without manual annotation.
This method offers a promising direction for unsupervised multimedia data retrieval.