ID	原文	译文
40096	为解决行人再识别的噪声标签问题，本文结合噪声、非噪声数据训练差异化特征，提出一种噪声标签自适应的行人再识别方法，不需要使用额外的验证集以及噪声比例、类型等先验信息，完成对噪声数据的筛选过滤。	In order to solve the noise label problem of person re-identification, this paper combines noise and non-noise data to train differentiated features, and proposes a noise-label adaptive pedestrian re-identification method without using additional verification sets, noise ratio, types and other priors.
40097	此外，本文方法自适应地学习噪声样本权重，进一步降低噪声影响。	In addition, the method adaptively learns the weight of noise data to further reduce the influence.
40098	在含噪声的Market1501、DukeMTMC-reID两个数据集上，主流模型受噪声影响严重，本文提出的方法可以在此基础上提高约10%的平均精度。	On the noisy Market1501 and DukeMTMC-reID data sets, the state of the art is severely affected by noise. The proposed method can improve the evaluation index by about 10% on this basis.
40099	针对实际场景中人脸表情识别训练和测试数据来自不同场景从而导致识别性能显著下降的问题，提出了一种基于稀疏子空间迁移学习的跨域人脸表情识别方法。	In practical facial expression recognition systems, recognition rates will drop significantly when the data are collected from different scenarios. To tackle this problem, in this paper, we propose a sparse subspace transfer learning for cross-domain facial expression recognition.
40100	首先，引入稀疏重构的思想来获得一个共同的投影矩阵，同时对重构系数矩阵施加L2，1范数约束；	Firstly, inspired by the idea of sparse reconstruction, we aim to learn a common projection matrix, and impose an L2, 1-norm constraint on the reconstruction coefficient matrix.
40101	其次，引入图拉普拉斯正则化项来保留数据的局部判别结构；	Secondly, we introduce the Laplacian regularization to preserve the local discriminative structure.
40102	最后，利用源域丰富的标签信息，将样本投影到一个由标签信息引导的子空间中。	Lastly, by utilizing the rich label information of source domain, we tend to project the source samples into a subspace guided by the label information.
40103	在3个经典人脸表情数据集中进行了实验，结果表明所提方法在人脸表情识别中优于其他几种经典的子空间迁移学习方法。	We conduct extensive experiments on three popular facial expression datasets. The results show that our proposed method can outperform several state-of-the-art subspace transfer learning methods in facial expression recognition.
40104	基于监控视频的弱外观多目标跟踪是建设智慧生物实验室的一个重要内容。	Multi-object tracking with weak appearances based on the surveillance video is one important issue for intelligent biology laboratory.
40105	但是，由于遮挡、目标外观差别细微等因素的影响，容易出现漏检、误检等问题，导致跟踪失败。	However, due to the occlusion and subtle differences among objects, missing detection or false detection is prone to cause tracking failure.