The continuous improvement of human-computer interaction technology makes it
possible to compute emotions. In this paper, we introduce our submission to the
CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW).
Sentiment analysis in human-computer interaction should, as far as possible
Start with multiple dimensions, fill in the single imperfect emotion channel,
and finally determine the emotion tendency by fitting multiple results.
Therefore, We exploited multimodal features extracted from video of different
lengths from the competition dataset, including audio, pose and images.
Well-informed emotion representations drive us to propose a Attention-based
multimodal framework for emotion estimation. Our system achieves the
performance of 0.361 on the validation dataset. The code is available at
[this https URL].

本文介绍了一个基于多模态的情感估计框架，通过提取比赛数据集中的不同长度的视频的多模态特征，包括音频、姿态和图像，确定情感趋势。该系统在验证数据集中达到 0.361 的性能。