SPS Webinars

Radial sampling pattern is an important signal acquisition strategy in magnetic resonance imaging (MRI) owing to better immunity to motion-induced artifacts and less pronounced aliasing due to undersampling compared to the Cartesian sampling. 

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates.

Human centric visual analysis tasks are essential to computer vision since humans are the key element for cameras to analyze. In this talk, I will mainly focus on 4 visual analysis tasks on human hand, gesture, pose, and action respectively.

Graph neural networks (GNNs) have achieved impressive results in various graph learning tasks and they have found their way into many application domains. Despite their proliferation, our understanding of their robustness properties is still very limited. 

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this work explores the low-light image enhancement problem, where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene. 

Recent advances in multimodal processing have led to promising solutions for speech-processing tasks. One example is automatic speech recognition (ASR), which is a key component in current speech-based systems.