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Related Experiment Video

Updated: Aug 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

LCM-Captioner: A lightweight text-based image captioning method with collaborative mechanism between vision and text.

Qi Wang¹, Hongyu Deng¹, Xue Wu²

¹State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 19, 2023

Summary

This study introduces LCM-Captioner, a lightweight method for text-based image captioning (TextCap). It efficiently integrates visual and textual information for improved image understanding and reduced computational costs.

Keywords:

Collaborative attention mechanism Feature transformation Lightweight network Multimodal information Text-based image captioning

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Last Updated: Aug 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

SIVQ-LCM Protocol for the ArcturusXT Instrument

SIVQ-LCM Protocol for the ArcturusXT Instrument

Published on: July 23, 2014

Area of Science:

Computer Vision
Artificial Intelligence
Natural Language Processing

Background:

Existing image captioning models often overlook textual information within images.
Complex architectures in current methods lead to inefficiency and deployment challenges.
Text-based image captioning (TextCap) requires models to process both visual and textual content for deeper comprehension.

Purpose of the Study:

To develop a lightweight and efficient TextCap method balancing performance and computational cost.
To address the limitations of existing complex models in multimodal information integration.
To improve the semantic alignment between visual and textual data for enhanced image description.

Main Methods:

Proposed TextLighT: a feature-lightening transformation for learning low-dimensional multimodal representations and reducing memory usage.
Developed VTCAM (Visual-Text Collaborative Attention Module): a module for semantic alignment of visual and textual information.
Implemented a lightweight captioning method, LCM-Captioner, integrating TextLighT and VTCAM.

Main Results:

The proposed LCM-Captioner method demonstrates effectiveness on the TextCaps dataset.
TextLighT successfully maps features to lower dimensions, reducing memory costs.
VTCAM facilitates semantic alignment, uncovering key visual and textual content.

Conclusions:

LCM-Captioner offers a high-efficiency, high-performance solution for text-based image captioning.
The developed techniques effectively model the relationship between vision and text.
The method overcomes deployment challenges associated with complex architectures.