论文标题
学习渐进式模态共享的变压器，用于有效的可见红外人员重新识别
Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification
论文作者
论文摘要
在复杂的模态变化下，可见的红外人员重新识别（VI-REID）是一项具有挑战性的检索任务。现有的方法通常着重于提取区分视觉特征，同时忽略不同方式之间视觉特征的可靠性和共同点。在本文中，我们提出了一个新颖的深度学习框架，称为“渐进式模态共享变压器”（PMT），以实现有效的Vi-Reid。为了减少模态差距的负面影响，我们首先将灰度图像作为辅助模式，并提出渐进式学习策略。然后，我们提出了一种模态共享的增强损失（MSEL），以指导模型探索来自模态共享特征的更可靠的身份信息。最后，为了解决较大的阶层内差异和小级别差异的问题，我们提出了一个歧视性中心损失（DCL）与MSEL相结合，以进一步改善对可靠特征的歧视。 SYSU-MM01和REGDB数据集的广泛实验表明，我们所提出的框架的性能要比大多数最先进的方法更好。对于模型复制，我们在https://github.com/hulu88/pmt上发布源代码。
Visible-Infrared Person Re-Identification (VI-ReID) is a challenging retrieval task under complex modality changes. Existing methods usually focus on extracting discriminative visual features while ignoring the reliability and commonality of visual features between different modalities. In this paper, we propose a novel deep learning framework named Progressive Modality-shared Transformer (PMT) for effective VI-ReID. To reduce the negative effect of modality gaps, we first take the gray-scale images as an auxiliary modality and propose a progressive learning strategy. Then, we propose a Modality-Shared Enhancement Loss (MSEL) to guide the model to explore more reliable identity information from modality-shared features. Finally, to cope with the problem of large intra-class differences and small inter-class differences, we propose a Discriminative Center Loss (DCL) combined with the MSEL to further improve the discrimination of reliable features. Extensive experiments on SYSU-MM01 and RegDB datasets show that our proposed framework performs better than most state-of-the-art methods. For model reproduction, we release the source code at https://github.com/hulu88/PMT.
