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Learning from data

Estimation in Multi-Object State Space Model

By: 
Dr. Ba-Ngu Vo

A brief introduction to state estimation in multi-object system that arises from applications where the number of objects and their states are unknown and vary randomly with time. State space model (SSM) is a fundamental concept in system theory that permeated through many fields of study.

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Empirical Wavelets

By: 
Dr. Jérôme Gilles

We design a data-driven wavelet transform, called the empirical wavelet transform, which permits to extract very accurate time-frequency information from signals, or features from images.

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Facial Expression Analysis with Attention Mechanism

By: 
Dr. Jiabei Zeng

We develop algorithms to analyzing facial expression by learning from the data. Since local characters of muscle movements play an important role in distinguishing facial expression by machines, we explore the local characters of facial expressions by introducing the attention mechanism in both supervised and self-supervised supervised manners. Our methods is experimentally shown to be effective on facial expression recognition with occlusions and facial action unit detection.

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