8 accepted papers
Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs) like CLIP for various downstream tasks. Despite their success, current VLM-based facial expression recognition (FER) methods struggle to capture fine-grained textual-visual relationships, which are essential f…
This paper focuses on detecting the central nodes in a graph from partially observed graph signals with unknown graph topology. We follow a general model which considers observed data as filtered graph signals with excitation driven from some external sources shaped by a possibly low-rank influence…
This paper considers learning a product graph from multi-attribute graph signals. Our work is motivated by the widespread presence of multilayer networks that feature interactions within and across graph layers. Focusing on a product graph setting with homogeneous layers, we propose a bivariate poly…
This paper proposes a mixture model of low pass filtered graph signals. Our aim is to jointly estimate the eigen-centrality vectors for the underlying graphs and identify the graph signal samples with their corresponding graphs, without knowing the graph topology a-priori. The problem is challenging…
Recently, the stability of graph filters has been studied as one of the key theoretical properties driving the highly successful graph convolutional neural networks (GCNs). The stability of a graph filter characterizes the effect of topology perturbation on the output of a graph filter, a fundamenta…
This paper is concerned with the blind identification of graph filters from graph signals. Our aim is to determine if the graph filter generating the graph signals is first-order lowpass without knowing the graph topology. Notice that lowpass graph filter is a common prerequisite for applying graph…
This paper considers the PageRank centrality computation problem on large graphs. We study asynchronous and distributed algorithms which are operated by aggregating information from local neighbors iteratively. Unlike prior works which rely on stochastic gradient descent (SGD) applied on a least squ…
This paper considers blind methods for centrality estimation from graph signals.We model graph signals as the outcome of an unknown low-pass graph filter excited with influences governed by a sparse sub-graph.This model is compatible with a number of data generation process on graphs, including stoc…