Visible infrared person re-identification (VI-ReID) exposes considerable challenges because of the modality gaps between the person images captured by daytime visible cameras and nighttime infrared cameras. Several fully-supervised VI-ReID methods have improved the performance with extensive labeled heterogeneous images. However, the identity of the person is difficult to obtain in real-world situations, especially at night. Limited known identities and large modality discrepancies impede the effectiveness of the model to a great extent. In this paper, we propose a novel Semi-Supervised Learning framework with Heterogeneous Distribution Consistency (HDC-SSL) for VI-ReID. Specifically, through investigating the confidence distribution of heterogeneous images, we introduce a Gaussian Mixture Model-based Pseudo Labeling (GMM-PL) method, which adaptively adjusts different thresholds for each modality to label the identity. Moreover, to facilitate the representation learning of unutilized data whose prediction is lower than the threshold, Modality Consistency Regularization (MCR) is proposed to ensure the prediction consistency of the cross-modality pedestrian images and handle the modality variance. Extensive experiments with different label settings on two VI-ReID datasets demonstrate the effectiveness of our method. Particularly, HDC-SSL achieves competitive performance with state-of-the-art fully-supervised VI-ReID methods on RegDB dataset with only 1 visible label and 1 infrared label per class.

中文翻译：

---

具有异质分布一致性的半监督学习可见红外行人重识别


由于白天可见光摄像机和夜间红外摄像机捕获的人物图像之间存在模态差异，可见红外行人重新识别（VI-ReID）面临着相当大的挑战。几种完全监督的 VI-ReID 方法提高了大量标记异构图像的性能。然而，在现实世界中，尤其是在晚上，很难获得该人的身份。有限的已知身份和较大的模态差异在很大程度上阻碍了模型的有效性。在本文中，我们为 VI-ReID 提出了一种新颖的具有异构分布一致性的半监督学习框架（HDC-SSL）。具体来说，通过研究异构图像的置信度分布，我们引入了一种基于高斯混合模型的伪标记（GMM-PL）方法，该方法自适应地调整每种模态的不同阈值来标记身份。此外，为了促进预测低于阈值的未利用数据的表示学习，提出了模态一致性正则化（MCR）来确保跨模态行人图像的预测一致性并处理模态方差。在两个 VI-ReID 数据集上使用不同标签设置进行的广泛实验证明了我们方法的有效性。特别是，HDC-SSL 在 RegDB 数据集上通过最先进的完全监督的 VI-ReID 方法实现了具有竞争力的性能，每类仅 1 个可见标签和 1 个红外标签。