FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference

TL;DR

FV2ES is a fully end-to-end multimodal video emotion recognition system that enhances acoustic modality contribution, improves efficiency, and reduces computational costs, enabling fast and effective inference on high-resolution videos.

Contribution

The paper introduces FV2ES, a novel multimodal system that combines hierarchical attention, multi-scale visual extraction, and integrated pre-processing for improved emotion recognition.

Findings

Outperforms existing models on IEMOCAP and CMU-MOSEI datasets.

Achieves higher efficiency with multi-scale visual extraction.

Reduces computational costs and storage through integrated pre-processing.

Abstract

In the latest social networks, more and more people prefer to express their emotions in videos through text, speech, and rich facial expressions. Multimodal video emotion analysis techniques can help understand users' inner world automatically based on human expressions and gestures in images, tones in voices, and recognized natural language. However, in the existing research, the acoustic modality has long been in a marginal position as compared to visual and textual modalities. That is, it tends to be more difficult to improve the contribution of the acoustic modality for the whole multimodal emotion recognition task. Besides, although better performance can be obtained by introducing common deep learning methods, the complex structures of these training models always result in low inference efficiency, especially when exposed to high-resolution and long-length videos. Moreover, the lack of a fully end-to-end multimodal video emotion recognition system hinders its application. In this paper, we designed a fully multimodal video-to-emotion system (named FV2ES) for fast yet effective recognition inference, whose benefits are threefold: (1) The adoption of the hierarchical attention method upon the sound spectra breaks through the limited contribution of the acoustic modality and outperforms the existing models' performance on both IEMOCAP and CMU-MOSEI datasets; (2) the introduction of the idea of multi-scale for visual extraction while single-branch for inference brings higher efficiency and maintains the prediction accuracy at the same time; (3) the further integration of data pre-processing into the aligned multimodal learning model allows the significant reduction of computational costs and storage space.