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Updated: Nov 2, 2025

Cross-Modal Multivariate Pattern Analysis

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Published on: November 9, 2011

Universal Weighting Metric Learning for Cross-Modal Retrieval.

Jiwei Wei, Yang Yang, Xing Xu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 14, 2021

Summary

This study introduces a universal weighting metric learning framework for cross-modal retrieval. The novel polynomial loss functions improve the ability to mine informative pairs, significantly boosting retrieval performance across various datasets.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Cross-modal retrieval, matching data from different sources (e.g., images and text), is a growing research area.
Effective metric learning is crucial for cross-modal retrieval, but existing methods are underexplored for this task.

Purpose of the Study:

To develop a universal weighting metric learning framework for cross-modal retrieval.
To introduce novel polynomial loss functions that enhance the mining and weighting of informative data pairs.

Main Methods:

A universal weighting metric learning framework was developed to sample informative pairs and assign weights based on similarity scores.
Two polynomial loss functions were introduced: self-similarity polynomial loss and relative-similarity polynomial loss.
These losses associate weights with similarity scores using polynomial functions.

Main Results:

The proposed framework and polynomial losses were applied to existing cross-modal retrieval methods.
Experiments demonstrated significant performance improvements on image-text and video-text retrieval datasets.
The method showed a noticeable boost in retrieval accuracy across multiple benchmark datasets.

Conclusions:

The developed universal weighting metric learning framework effectively enhances cross-modal retrieval.
The novel self-similarity and relative-similarity polynomial losses offer a flexible and effective approach to improve retrieval performance.
The proposed methods provide a valuable contribution to the field of cross-modal retrieval.