M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation

Abstract

Emotion Recognition in Conversations (ERC) is crucial in developing sympathetic human-machine interaction. In conversational videos, emotion can be present in multiple modalities, i.e., audio, video, and transcript. However, due to the inherent characteristics of these modalities, multi-modal ERC has always been considered a challenging undertaking. Existing ERC research focuses mainly on using text information in a discussion, ignoring the other two modalities. We anticipate that emotion recognition accuracy can be improved by employing a multi-modal approach. Thus, in this study, we propose a Multi-modal Fusion Network (M2FNet) that extracts emotion-relevant features from visual, audio, and text modality. It employs a multi-head attention-based fusion mechanism to combine emotion-rich latent representations of the input data. We introduce a new feature extractor to extract latent features from the audio and visual modality. The proposed feature extractor is trained with a novel adaptive marginbased triplet loss function to learn emotion-relevant features from the audio and visual data. In the domain of ERC, the existing methods perform well on one benchmark dataset but not on others. Our results show that the proposed M2FNet architecture outperforms all other methods in terms of weighted average F1 score on well-known MELD and IEMOCAP datasets and sets a new state-of-theart performance in ERC.

Detection of Inferior Vena Cava in Ultrasound Scans through a Deep Learning Model

“…This study aims to introduce a deep learning algorithm to deal with the constraints imposed by user choices and turn our algorithms into completely automated software programs. We chose deep learning to detect IVC in US scans because it has produced impressive results in many image-recognition tasks [15][16][17][18]. Convolutional neural networks (CNNs) have been extensively used in a wide range of applications, including human pose estimation [19], medical image analysis [20,21], and other computer vision tasks [22].…”