Margin-enhanced average precision optimization for visible-infrared person re-identification

Abstract

Average precision-based loss functions are effective for training deep neural networks in ranking tasks and single-modality person re-identification. However, they have difficulty in uniformly ranking cross-modality samples due to high intra-modality similarity. To address this limitation, this paper proposes a Margin-Enhanced Average Precision (MEAP) optimization approach for cross-modality person re-identification. MEAP integrates average precision optimization with inter-class and cross-modality margin parameters. The inter-class margin improves performance by increasing class separation, while the cross-modality margin enhances performance by prioritizing cross-modality positive samples. Additionally, we propose an innovative algorithm to augment visible and infrared person images using horizontal stripes, aiming to bridge the gap between the two modalities by creating diverse and enriched training data. Experiments on two public datasets demonstrate the effectiveness of our approach in comparison to state-of-the-art methods. MEAP and horizontal stripe augmentation significantly improve accuracy and robustness in matching individuals across different modalities. The code is available at: https://github.com/NihatTekeli/meapnet