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Related Experiment Video

Updated: Jun 28, 2026

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

Semantic-Aware Multimodal Collaborative Learning for Unsupervised Visible-Infrared Person Re-Identification.

Xueping Wang, Shixi Luo, Min Liu

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

|April 23, 2026

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This study introduces a Semantic-aware Multimodal Collaborative Learning (SAMCL) framework to improve unsupervised visible-infrared person reidentification (VI-ReID). SAMCL effectively bridges the modality gap and refines feature learning, achieving state-of-the-art results on multiple datasets.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Unsupervised visible-infrared person reidentification (VI-ReID) faces challenges due to the modality gap between visible and infrared images.
Existing methods often use noisy one-hot pseudo-labels, limiting semantic understanding and robustness.

Purpose of the Study:

To propose a novel Semantic-aware Multimodal Collaborative Learning (SAMCL) framework for unsupervised VI-ReID.
To address the limitations of existing methods by enhancing cross-modal and intra-modality feature learning.

Main Methods:

Developed a Modality-aware Semantic Fusion (MSF) module to integrate complementary semantic details across modalities, creating enriched cross-modal supervision.
Introduced a Dynamic Contrastive Learning (DCL) module for refined intra-modality feature learning by aligning samples with dynamic centroids.

Related Experiment Videos

Last Updated: Jun 28, 2026

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

Combined MSF and DCL modules within the SAMCL framework for robust unsupervised VI-ReID.

Main Results:

Achieved state-of-the-art (SOTA) performance on multiple benchmark datasets.
On SYSU-MM01 (All Search), achieved 68.68% Rank-1 accuracy, surpassing SOTA by 3.48%.
On RegDB (Visible-to-Infrared), achieved 94.47% Rank-1 accuracy, outperforming SOTA by 3.57%.
On LLCM (Visible-to-Infrared), achieved 50.6% Rank-1 accuracy, outperforming SOTA by 3.7%.

Conclusions:

The proposed SAMCL framework effectively overcomes the modality gap in unsupervised VI-ReID.
SAMCL demonstrates superior performance and robustness compared to existing methods.
The framework minimizes reliance on noisy pseudo-labels through effective multimodal collaboration.