Frequency Domain Nuances Mining for Visible-Infrared Person Re-identification
CoRR(2024)
摘要
The key of visible-infrared person re-identification (VIReID) lies in how to
minimize the modality discrepancy between visible and infrared images. Existing
methods mainly exploit the spatial information while ignoring the
discriminative frequency information. To address this issue, this paper aims to
reduce the modality discrepancy from the frequency domain perspective.
Specifically, we propose a novel Frequency Domain Nuances Mining (FDNM) method
to explore the cross-modality frequency domain information, which mainly
includes an amplitude guided phase (AGP) module and an amplitude nuances mining
(ANM) module. These two modules are mutually beneficial to jointly explore
frequency domain visible-infrared nuances, thereby effectively reducing the
modality discrepancy in the frequency domain. Besides, we propose a
center-guided nuances mining loss to encourage the ANM module to preserve
discriminative identity information while discovering diverse cross-modality
nuances. To the best of our knowledge, this is the first work that explores the
potential frequency information for VIReID research. Extensive experiments show
that the proposed FDNM has significant advantages in improving the performance
of VIReID. Specifically, our method outperforms the second-best method by 5.2%
in Rank-1 accuracy and 5.8% in mAP on the SYSU-MM01 dataset under the indoor
search mode, respectively. Besides, we also validate the effectiveness and
generalization of our method on the challenging visible-infrared face
recognition task. The code will be available.
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