This paper presents a tensor multi-task model for person re-identification (Re-ID). Due to discrepancy among cameras, our approach regards Re-ID from multiple cameras as different but related classification tasks, each task corresponding to a specific camera. In each task, we distinguish the person identity as a one-vs-all linear classification problem, where one classifier is associated with a specific person. By constructing all classifiers into a task-specific projection matrix, the proposed method could utilize all the matrices to form a tensor structure, and jointly train all the tasks in a uniform tensor space. In this space, by assuming the features of the same person under different cameras are generated from a latent subspace, and different identities under the same perspective share similar patterns, the high-order correlations, not only across different tasks but also within a certain task, can be captured by utilizing a new type of low-rank tensor constraint. Therefore, the learned classifiers transform the original feature vector into the latent space, where feature distributions across cameras can be well-aligned. Moreover, this model can be incorporated into multiple visual features to boost the performance, and easily extended to the unsupervised setting. Extensive experiments and comparisons with recent Re-ID methods manifest the competitive performance of our method.

中文翻译：

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用于人员重新识别的Tensor多任务学习。

本文提出了一个张量多任务模型用于人员重新识别（Re-ID）。由于摄像机之间的差异，我们的方法将来自多个摄像机的Re-ID视为不同但相关的分类任务，每个任务对应于一个特定摄像机。在每个任务中，我们将人的身份区分为一对一线性分类问题，其中一个分类器与特定人相关联。通过将所有分类器构建到特定任务的投影矩阵中，该方法可以利用所有矩阵形成张量结构，并在统一的张量空间中共同训练所有任务。在这个空间中，假设从潜子空间生成同一个人在不同摄像机下的特征，并且同一视角下的不同身份共享相似的模式，那么高阶相关性 利用新型的低秩张量约束，不仅可以捕获跨不同任务的任务，而且还可以捕获特定任务的任务。因此，学习到的分类器将原始特征向量转换为潜在空间，从而可以很好地对齐相机之间的特征分布。此外，该模型可以合并到多个视觉功能中以提高性能，并且可以轻松扩展到无人监督的设置。大量的实验和与最新Re-ID方法的比较证明了我们方法的竞争力。该模型可以合并到多个视觉功能中以提高性能，并且可以轻松扩展到无人监督的设置。大量的实验和与最新Re-ID方法的比较证明了我们方法的竞争力。该模型可以合并到多个视觉功能中以提高性能，并且可以轻松扩展到无人监督的设置。大量的实验和与最新Re-ID方法的比较证明了我们方法的竞争力。