Harmonizing Metric Discrepancy for Cross-Modal Object Re-Identification

Visible and infrared cross-modal re-identification tasks often encounter significant modal discrepancies, which undermine the effectiveness of feature extraction and compromise the reliability of similarity metrics. These discrepancies pose a substantial challenge for accurately matching data across different modalities. To address these issues, we propose a novel approach centered on the maximum mean metric discrepancy (MMMD). We leverage kernel-based statistical techniques to effectively capture and quantify the disparities in cross-modal metrics, providing a robust framework for aligning metrics from different modalities. Building upon the foundation of MMMD, we develop the metric discrepancy harmonization (MDH) method. This method integrates a temperature-controlled optimization technique designed to enhance metric alignment across various modal configurations, ensuring more consistent and reliable performance. By focusing on metric alignment, our approach enhances the accuracy of cross-modal re-identification tasks. Comprehensive evaluations on the LLCM, RGBN300, and SYSU-MM01 datasets demonstrate that our approach achieves state-of-the-art performance.