Landmark recognition stands as a prominent classification challenge within the domain of vision and perception, involving the identification and localization of landmarks in images. However, existing landmark recognition methods often fall short in delivering satisfactory performance. A critical issue lies in the lack of simultaneous exploration of local, regional, and global modeling. Intuitively, the local details, regional features, and global structure within an image, especially of a building, significantly contribute to landmark recognition. To address this issue, we propose a novel approach, termed Hierarchical Context Modeling Network (HCMNet), for landmark recognition. In our methodology, we introduce a Hierarchical Context Modeling Block (HCMB) designed with a triplet-branch structure to model image context in a hierarchical manner. These branches consist of the local branch, which leverages a convolutional layer to capture local details, the regional branch focused on extracting regional features through large-kernel asymmetrical convolutional layers, and the global branch aims at encompassing the broader context by incorporating channel attention. To enhance the modeling capacity, we assemble multiple HCMBs into the HCMNet, creating a hierarchical structure. Comprehensive experiments validate the efficacy of the proposed HCMNet for landmark recognition, showcasing its superior performance compared to current methods. The proposed hierarchical context modeling approach proves instrumental in capturing the intricate features essential for accurate landmark recognition.

Clickbait is a sort of news that contains low-quality content but intriguing or overstated titles. Most of the existing studies usually utilize titles to detect clickbait and suffer from poor performance. However, from the perspective of user cognition, the essence of clickbait news is due to the fact that the body contents fall short of the titles’ expectations. So, in addition to the explicit features of titles, many extra informative signals could help to distinguish clickbait. In this article, we proposed a novel Multiview learning approach-based ClickBait Detection (MCBD) model that takes into account useful signals from both titles and body contents. From the view of titles, a multilayer gated convolutional network is employed to learn the local- and long-distance dependence relationships between words. From the view of body contents, we first extract the key topic sentences from the body content and then we developed a novel attention-fused deep relevance matching network (ARMN) to thoroughly mine the similarity between titles and body contents. Combined with the information from the views of titles and body contents, our model can distinguish well-hidden clickbait news. Finally, extensive experiments were conducted on two real-world datasets to demonstrate that the MCBD model outperforms the state-of-the-art models, improving the final performance by 3% in terms of the F1-score. The ablation studies demonstrate that the relevancy between titles and body contents can improve the model’s performance by 1.7% on the F1-score.

Both neighbourhood preserving embedding (NPE) and sparsity preserving projection (SPP) are unsupervised learning methods, where NPE can preserve the local neighbourhood information of a given dataset and SPP can preserve the sparsely reconstructive relationship of the dataset. However, it is not satisfactory when applying the two methods to classification tasks. First, a modified SPP is presented here. Then this study proposes a supervised sparse neighbourhood preserving embedded algorithm (SSNPE) based on NPE, the modified SPP and the label information of a given task. SSNPE inherits the merits of NPE and SPP, which can preserve not only the local neighbourhood information but also the sparsely reconstructive relationship. The connection between SSNPE and both NPE and SPP is discussed. Experimental results on the datasets of UCI, ORL and MNIST indicate that the proposed method is effective.

Neighborhood preserving embedding (NPE) is an approximation to locally linear embedding (LLE), which has an ability to preserve local neighborhood structure on data manifold. As an unsupervised dimensionality reduction method, NPE can be applied to face recognition for preprocessing. However, NPE could not utilize the label information in the classification tasks. To make the data in a reduced subspace separable, this paper proposes a supervised neighborhood preserving embedding which could learn a projection matrix by using both the geometrical manifold structure and the label information of the given data. In addition, the projection matrix could be found by solving a linear set of equations. Experimental results on ORL and Yale face image datasets show that the proposed method has a high recognition rate.