Behavior Analysis in the Wild

Behavior analysis in the wild focuses on automatically recognizing human emotions and affective states from unconstrained real-world videos and audio. Current research heavily utilizes deep learning, particularly employing transformer networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) like LSTMs, often in multi-task and multi-modal frameworks that integrate visual, audio, and even textual data. These advancements aim to improve the accuracy and robustness of emotion recognition systems, with significant implications for applications such as human-computer interaction, mental health monitoring, and personalized experiences.