In this paper, we present the solution to the Emotional Mimicry Intensity
(EMI) Estimation challenge, which is part of 6th Affective Behavior Analysis
in-the-wild (ABAW) Competition.The EMI Estimation challenge task aims to
evaluate the emotional intensity of seed videos by assessing them from a set of
predefined emotion categories (i.e., "Admiration," "Amusement,"
"Determination," "Empathic Pain," "Excitement," and "Joy").

本文介绍了解决情感模仿强度（EMI）估计挑战的方法，该挑战是第六届野外情感行为分析竞赛（ABAW）的一部分。EMI 估计挑战任务旨在通过从预定义的情绪类别（即 “崇拜”，“娱乐”，“决心”，“共情痛苦”，“兴奋” 和 “喜悦”）中评估它们来评估种子视频的情感强度。

音视情感模仿强度估计的高效特征提取和后期融合策略

Efficient Feature Extraction and Late Fusion Strategy for Audiovisual  Emotional Mimicry Intensity Estimation

Large Language Models (LLM) have recently been shown to perform well at
various tasks from language understanding, reasoning, storytelling, and
information search to theory of mind. In an extension of this work, we explore
the ability of GPT-4 to solve tasks related to emotion prediction. GPT-4
performs well across multiple emotion tasks; it can distinguish emotion
theories and come up with emotional stories. We show that by prompting GPT-4 to
identify key factors of an emotional experience, it is able to manipulate the
emotional intensity of its own stories. Furthermore, we explore GPT-4's ability
on reverse appraisals by asking it to predict either the goal, belief, or
emotion of a person using the other two. In general, GPT-4 can make the correct
inferences. We suggest that LLMs could play an important role in affective
modeling; however, they will not fully replace works that attempt to model the
mechanisms underlying emotion-related processes.

GPT-4 可以进行情感预测和情感强度的操纵，以及通过反向评估进行目标、信念和情感的预测。然而，它不能完全取代试图建模情感相关过程的作品，尽管语言模型在情感建模中有重要作用。