Accepted Papers:

• Deep Recurrent Multi-instance Learning with Spatio-temporal Features for Engagement Intensity Prediction.
• Automatic Engagement Prediction with GAP Feature.
• Predicting Engagement Intensity in the Wild Using Temporal Convolutional Network.
• An Ensemble Model Using Face and Body Tracking for Engagement Detection.
• Group-Level Emotion Recognition using Hybrid Deep Models based on Faces, Scenes, Skeletons and Visual Attentions.
• Cascade Attention Networks For Group Emotion Recognition with Face, Body and Image Cues.
• Group-Level Emotion Recognition using Deep Models with A Four-stream Hybrid Network.
• An Attention Model for group-level emotion recognition.
• Multi-Feature Based Emotion Recognition for Video Clips.
• Video-based Emotion Recognition Using Deeply-Supervised Neural Networks.
• An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets.
• Multiple Spatio-temporal Feature Learning for Video-based Emotion Recognition in the Wild