A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Recently, graph neural networks have attracted great attention and achieved prominent
performance in various research fields. Most of those algorithms have assumed pairwise …

Despite the advances in machine learning techniques, similarity assessment among
multimedia data remains a challenging task of broad interest in computer science …

Although existing semi-supervised learning models achieve remarkable success in learning
with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled …

Modeling feature interactions is of crucial significance to high-quality feature engineering on
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …

Tabular structures in documents offer a complementary dimension to the raw textual data,
representing logical or quantitative relationships among pieces of information. In digital mail …

Graph theory is a fundamental branch of mathematics that studies networks consisting of
nodes (vertices) and their connections (edges). Extensive research has been conducted on …

In this paper we revisit diffusion processes on affinity graphs for capturing the intrinsic
manifold structure defined by pairwise affinity matrices. Such diffusion processes have …

In this paper, we propose an extremely efficient algorithm for visual re-ranking. By
considering the original pairwise distance in the contextual space, we develop a feature …

This work studies the unsupervised re-ranking procedure for object retrieval and person re-
identification with a specific concentration on an ensemble of multiple metrics (or …