Sentiment analysis aims to automatically uncover the underlying attitude that we hold
towards an entity. The aggregation of these sentiment over a population represents opinion …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

Natural human-computer interaction and audio-visual human behaviour sensing systems,
which would achieve robust performance in-the-wild are more needed than ever as digital …

Conditional generative adversarial networks (cGAN) have led to large improvements in the
task of conditional image generation, which lies at the heart of computer vision. The major …

Continuous dimensional models of human affect, such as those based on valence and
arousal, have been shown to be more accurate in describing a broad range of spontaneous …

Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of
sentiment and emotion, as well as physiological-emotion and emotion-based stress …

This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in
2011 (with the release of the first Kinect device) to run challenges related to human …

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion
research. The high variety of possible 'in-the-wild'properties makes large datasets such as …

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed
proceedings of the 21st International Conference on Medical Image Computing and …

In this work, we study the problem of common and unique feature extraction from noisy data.
When we have $ N $ observation matrices from $ N $ different and associated sources …