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Related Experiment Video

Updated: Sep 30, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation.

Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 17, 2022

Summary

This summary is machine-generated.

This study introduces cross-modal learning for domain adaptation, enhancing machine learning with scarce labels by enforcing consistency between different data types like images and point clouds. The novel approach significantly improves 3D semantic segmentation across various challenging scenarios.

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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Domain adaptation is crucial for machine learning with limited labeled data.
Most existing methods focus on single data modalities (e.g., images).
Multi-modal datasets offer rich information but are underutilized for domain adaptation.

Purpose of the Study:

To propose a novel cross-modal learning strategy for domain adaptation.
To leverage multi-modal data (2D images, 3D point clouds) for improved learning.
To enhance 3D semantic segmentation performance in varied domain adaptation scenarios.

Main Methods:

Developed a cross-modal learning framework enforcing prediction consistency between modalities.
Utilized mutual mimicking to align predictions on unlabeled target-domain data.
Constrained the network for accurate predictions on labeled data and cross-modal consistency.

Main Results:

Demonstrated effectiveness in unsupervised and semi-supervised domain adaptation settings.
Significantly improved 3D semantic segmentation over uni-modal baselines.
Evaluated across diverse scenarios including synthetic-to-real and varying environmental conditions.

Conclusions:

Cross-modal learning is a powerful strategy for domain adaptation.
The proposed method effectively leverages multi-modal data for enhanced performance.
This approach offers significant improvements for 3D semantic segmentation tasks.