4 accepted papers
Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying human emotional states by combining data from multiple different modalities (e.g., audio, images, text, video, etc.). Specifically, human emotional expressions are often complex and diverse, and these complex…
Multimodal Emotion Recognition (MER) combines technologies from multiple fields (e.g., computer vision, natural language processing, and audio signal processing), aiming to infer an individual's emotional state by analyzing information from different sources (i.e., video, audio, and text). Compared
In recent years, whole slide image (WSI)-based survival analysis has attracted much attention. In practice, WSIs usually come from different hospitals (or domains) and may have significant differences. These differences generally result in large gaps in distribution between different WSI domains and…
Efficiently capturing consistent and complementary semantic features in context is crucial for Multimodal Emotion Recognition in Conversations (MERC). However, limited by the over-smoothing or low-pass filtering characteristics of spatial graph neural networks, are insufficient to accurately capture…