In multi-class categorization tasks, knowledge about the classes’ semantic relationships can provide valuable information beyond the class labels themselves. However, existing techniques focus on preserving the semantic distances between classes (e.g., according to a given object taxonomy for visual recognition), limiting the influence to pairwise structures. We propose to model analogies that ...

Manifold learning is a powerful tool for solving nonlinear dimension reduction problems. By assuming that the high-dimensional data usually lie on a low-dimensional manifold, many algorithms have been proposed. However, most algorithms simply adopt the traditional graph Laplacian to encode the data locality, so the discriminative ability is limited and the embedding results are not always suita...

Among the adaptive-grid methods, redistribution and embedding techniques have been the focus of more attention by researchers. Simultaneous or combined adaptive techniques have also been used. This paper describes a combination of adaptive-grid embedding and redistribution methods on semi-structured grids for two-dimensional invisid flows. Since the grid is semi-structured, it is possible to us...

Despite significant recent advances in image classification, fine-grained classification remains a challenge. In the present paper, we address the zero-shot and few-shot learning scenarios as obtaining labeled data is especially difficult for fine-grained classification tasks. First, we embed state-of-the-art image descriptors in a label embedding space using side information such as attributes...

This paper investigates using Semidefinite Embedding (SDE) to visualize data collected from a folksonomy. The del.icio.us social bookmarking service is a perfect example of a folksonomy; a community of users label websites with descriptive tags. Each tag exists in a high-dimensional space corresponding to the frequency of use of that tag among all the users of the system. We are motivated by th...

In this paper, a faster method for embedding cryptographic information in the image ispresented by expressing the concept of latent prints (Steganography). Data is encrypted bytwo methods before embedding to increase reliability. Then they are embedded into the imageby a button, a method has been expressed to reduce potential noise impact on the wayinformation is encoded.

Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to exponential growth of output space. It confronts great challenge for exploration latent label relationship and intrinsic correlation between feature spaces. MLC gave rise a framework named compression (LC) obtain compact space efficient learning. Nevertheless, mos...

Word sense disambiguation tries to learn the appropriate of an ambiguous word in a given context. The existing pre-trained language methods and based on multi-embeddings did not explore power unsupervised embedding sufficiently. In this paper, we discuss capsule network-based approach, taking advantage capsule’s potential for recognizing highly overlapping features dealing with segmentation. We...