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Related Experiment Video

Updated: Jul 6, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Bidirectional visual-tactile cross-modal generation using latent feature space flow model.

Yu Fang¹, Xuehe Zhang¹, Wenqiang Xu²

¹State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 2, Yikuang Street, Nangang District, Harbin, 150001, Heilongjiang, China.

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

|December 30, 2023

Summary

This summary is machine-generated.

This study introduces a novel method for bidirectional visual-tactile mapping using a single model. The approach achieves high data similarity and classification accuracy, advancing cross-modal AI research.

Keywords:

Cross-modal Deep learning Flow model Visual-tactile data

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Last Updated: Jul 6, 2025

Cross-Modal Multivariate Pattern Analysis

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Published on: November 9, 2011

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

Published on: May 23, 2019

Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision
Haptics

Background:

Existing cross-modal mapping studies predominantly focus on unidirectional approaches.
Human brain research highlights visual-tactile cross-modal bidirectional mapping.
Limited exploration exists for bidirectional mapping between visual and tactile data in AI.

Purpose of the Study:

To develop a novel approach for bidirectional mapping between visual and tactile data.
To enable a single model to perform cross-modal bidirectional mapping.
To address the limitations of unidirectional mapping in existing studies.

Main Methods:

Utilized separate Variational AutoEncoder (VAE) models for visual and tactile data.
Introduced a conditional flow model built on the VAE latent feature space.
Enabled cross-modal bidirectional mapping between visual and tactile data using a unified model.

Main Results:

Achieved high Structural Similarity Index (SSIM) for generated visual (0.58) and tactile (0.80) data.
Demonstrated excellent classification accuracy on generated data (visual: 91.60%, tactile: 88.05%).
Obtained notable zero-shot classification accuracy between generated data and language (visual: 44.49%, tactile: 45.03%).

Conclusions:

The proposed method is the first to achieve bidirectional visual-tactile mapping using a single model.
The approach shows strong performance in data generation and cross-modal classification.
The model and code will be publicly released to facilitate further research.