Events

Speaker:
Professor ZHANG Hao, Richard
Amazon Scholar, Professor
School of Computing Science, Simon Fraser University, Canada

Abstract:

Neural implicit representations are the immediate precursors to neural radiance fields (NeRF). In a short span of only four years, they have quickly become the representation of choice

Speaker:
Dr. HAO Cong, Callie
Assistant Professor
Department of Electrical and Computer Engineering (ECE), Georgia Institute of Technology (GaTech)

Abstract:

In this talk, we introduce two architectures, one for graph neural work (GNN) called FlowGNN, one for vision transformer (ViT) called Edge-MoE. In FlowGNN, a gene

Speaker:
Prof. Peter Kazanzides
Research  Professor
Department of Computing Science, Johns Hopkins University

Abstract:

The da Vinci Research Kit (dVRK) is an open research platform that couples open-source control electronics and software with the mechanical components of the da Vinci surgical robot. This presentatio

Speaker:
Prof. DAI Hong-Ning
Associate Professor
Department of Computing Science, Hong Kong Baptist University (HKBU)

Abstract:

The metaverse is essentially constructed by multiple technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), artificial intelligence (AI), digital twin (DT),

Speaker:
Prof. Xiaolong Wang
Associate Professor
Department of Electrical and Computer Engineering, University of California, San Diego

Abstract:

Robot learning has witnessed significant progress in terms of generalization in the past few years. At the heart of such a generalization, the advancement of representation l

Speaker:
Dr. LIU Sifei

Abstract:

Disentangled representation in computer vision refers to encoding visual data into distinct, independent factors. These representations are critical for enhancing interpretability, improving generalization across tasks, and enabling controlled manipulation of specific visual attributes.