Person re-identification (Re-ID) has achieved great success in single-domain. However, it remains a challenging task to adapt a Re-ID model trained on one dataset to another one. Unsupervised domain adaption (UDA) was proposed to migrate a model from a labeled source domain to an unlabeled target domain. The main difference in the cross-domain is different background styles. Although the style transfer approach effectively reduces inter-domain gaps, it ignores the reduction of intra-class differences. Clustering-based pipelines maintain state-of-the-art performance for UDA by learning domain-independent features; however, most existing models do not sufficiently exploit the rich unlabeled samples in target domains due to unsatisfactory clustering. Thus, we propose a novel local correlation ensemble model that focuses on the diversity of intra-class information and the reliability of class centers. Specifically, a pedestrian attention module is proposed to enable the encoder to pay more attention to the person’s features to relieve interference caused by the shared background style. Furthermore, we propose a priority-distance graph convolutional network (PDGCN) module that employs a graph convolutional network network to predict the priority of a node as a class center and then calculates the distance between nodes with high priority values to screen out the class center nodes. Finally, the encoder features (local) and PDGCN features (context-aware) are combined to perform person Re-ID. The results of experiments on the large-scale public Re-ID datasets verified the effectiveness of the proposed method.

The accurate detection of cracks in paintings, which generally portray rich and varying content, is a challenging task. Traditional crack detection methods are often lacking on recent acquisitions of paintings as they are poorly adapted to high-resolutions and do not make use of the other imaging modalities often at hand. Furthermore, many paintings portray a complex or cluttered composition, significantly complicating a precise detection of cracks when using only photographic material. In this paper, we propose a fast crack detection algorithm based on deep convolutional neural networks (CNN) that is capable of combining several imaging modalities, such as regular photographs, infrared photography and X-Ray images. Moreover, we propose an efficient solution to improve the CNN-based localization of the actual crack boundaries and extend the CNN architecture such that areas where it makes little sense to run expensive learning models are ignored. This allows us to process large resolution scans of paintings more efficiently. The proposed on-line method is capable of continuously learning from newly acquired visual data, thus further improving classification results as more data becomes available. A case study on multimodal acquisitions of the Ghent Altarpiece, taken during the currently ongoing conservation-restoration treatment, shows improvements over the state-of-the-art in crack detection methods and demonstrates the potential of our proposed method in assisting art conservators.

Multi-view subspace clustering methods used consensus and supplementary principles to learn the shared self-representation matrix or tensor have been applied to multiple fields. The existing advanced multi-view subspace clustering methods are mainly based on the extension of low-rank representation from matrix to tensor. However, the tensor optimization methods have two limitations: they cannot retain the local geometric structure of data features residing in multiple nonlinear subspaces; they represent the low-rank structure based on the tensor nuclear norm, which will cause undesirable low-rank approximation. To solve these problems, we propose a hyper-Laplacian regularized Nonconvex Low-rank Representation (HNLR) method for multi-view subspace clustering. HNLR uses hyper-Laplacian regularizer to capture the high-order local geometry structure of each view. In addition, by introducing a nonconvex Laplace function to replace the tensor nuclear norm, HNLR can greatly improve the approximate performance of the global low-rank structure. Based on the alternating direction method of multiplier, we design an effective alternate iteration strategy to optimize HNLR model. Experimental results on eight real datasets have proved the superiority of our proposed method.