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Related Experiment Video

Updated: Nov 8, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Video Moment Localization via Deep Cross-Modal Hashing.

Yupeng Hu, Meng Liu, Xiaobin Su

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 26, 2021

Summary

This study introduces a novel deep cross-modal hashing network for efficient video moment localization. The model enhances temporal context understanding and query relevance for improved video analysis.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Video moment localization is crucial for analyzing the growing volume of video data.
Existing methods face challenges in temporal context modeling, candidate generation, and scalability.

Purpose of the Study:

To develop an efficient and scalable deep end-to-end cross-modal hashing network for video moment localization.
To address limitations in temporal context modeling and intelligent moment candidate generation.

Main Methods:

A bidirectional temporal convolutional network (TCN) based video encoder generates moment candidates and representations.
An independent query encoder captures user intent.
A cross-modal hashing module projects video and query representations into a shared Hamming space for efficient matching.

Main Results:

The proposed model achieves superior performance compared to state-of-the-art methods on real-world datasets.
The hashing approach enables efficient and scalable video moment localization.
Offline hash code learning significantly improves practical efficiency.

Conclusions:

The deep end-to-end cross-modal hashing network effectively addresses challenges in video moment localization.
The model offers a promising solution for efficient and scalable video content analysis.
This approach enhances the understanding of temporal context and user relevance in video retrieval.