Computer Vision Estimation of Emotion Reaction Intensity in the Wild

Emotions play an essential role in human communication. Developing computer vision models for automatic recognition of emotion expression can aid in a variety of domains, including robotics, digital behavioral healthcare, and media analytics. There are three types of emotional representations which are traditionally modeled in affective computing research: Action Units, Valence Arousal (VA), and Categorical Emotions. As part of an effort to move beyond these representations towards more fine-grained labels, we describe our submission to the newly introduced Emotional Reaction Intensity (ERI) Estimation challenge in the 5th competition for Affective Behavior Analysis in-the-Wild (ABAW). We developed four deep neural networks trained in the visual domain and a multimodal model trained with both visual and audio features to predict emotion reaction intensity. Our best performing model on the Hume-Reaction dataset achieved an average Pearson correlation coefficient of 0.4080 on the test set using a pre-trained ResNet50 model. This work provides a first step towards the development of production-grade models which predict emotion reaction intensities rather than discrete emotion categories.

TempT: Temporal consistency for Test-time adaptation

In this technical report, we introduce TempT, a novel method for test time adaptation on videos by ensuring temporal coherence of predictions across sequential frames. TempT is a powerful tool with broad applications in computer vision tasks, including facial expression recognition (FER) in videos. We evaluate TempT's performance on the AffWild2 dataset as part of the Expression Classification Challenge at the 5th Workshop and Competition on Affective Behavior Analysis in the wild (ABAW). Our approach focuses solely on the unimodal visual aspect of the data and utilizes a popular 2D CNN backbone, in contrast to larger sequential or attention based models. Our experimental results demonstrate that TempT has competitive performance in comparison to previous years reported performances, and its efficacy provides a compelling proof of concept for its use in various real world applications.