Speakers

• Dr. Wei Hu — Peking University

Abstract

Geometric data acquired from real-world scenes, e.g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including autonomous driving, augmented and virtual reality, surveillance, etc. Due to irregular sampling patterns of most geometric data, traditional image/video processing methodologies are limited, while Graph Signal Processing (GSP)—a fast-developing field in the signal processing community—enables processing signals that reside on irregular domains. Further, GSP provides insightful spectral interpretations and domain knowledge for the recently developed Graph Neural Networks (GNNs), leading to interpretability and robustness of GNNs. In this talk, I will introduce our recent research projects to illustrate the power of graph spectral processing and analysis in terms of geometric structure learning, unsupervised graph representation learning and interpretable analysis via GSP-based prior knowledge. Beyond geometric data, I will also introduce our interpretable and lightweight transformer design for brain signals via balanced signed graph algorithm unrolling.

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