Scene text recognition is considered as a sequence labeling problem. For the text recognition task, the alignment between the scene text image and the output text is coincident, which means the latter characters corresponding to the image region will also be behind. However, the existing global attention-based method focuses too much irrelevant information which leads to alignment drift. Contrary, local attention selects the subset of feature representation most relevant to the current character. In this paper, we explore the local attention mechanism and attempt to replace the global attention to implement decoding. Therefore, we revise several variants of local attention methods and provide a comprehensive comparison, which is missing in the scene text recognition literature so far. Specially, we introduce two Heuristic approaches for Local Attention (HLA) and prove that monotonic alignment improves performance significantly. Evaluations on the benchmarks show that the local attention method outperforms the existing global attention methods.

Person re-identification (Re-ID) is a retrieval problem based on computer vision, playing an important role in surveillance applications where we tried to identify the same person among surveillance photographs. At present, most person re-identification technologies and methods are based on convolutional neural networks (CNNs). Vision Transformers are merged recently and tend to displace pure CNNs in various computer vision tasks. In this paper, we first try ResNet to accomplish the task, with MGN and other tricks to improve the precision, then we explore the Swin Transformer, a pure transformer-based model. We make a large scale unlabeled dataset in which people acting various activities by cutting pictures in videos from YouTube, DINO and MoBY are employed for ResNet and Swin Transformer separately to perform unsupervised pre-training for improving the generalization ability of the learned person re-identification feature representation. Finally, We also make a dataset based on border inspection BoderCheck by using DBSCAN to cluster unlabeled pictures with several adaptations, a multi-datasets training method and a data augmentation are proposed to tackle the half-length vs full-body matching problem. Extensive experiments indicate that our method achieves state-of-the-art results on several mainstream benchmarks and behave well on BoderCheck dataset.