零(语言学)
水准点(测量)
弹丸
情绪识别
计算机科学
人工智能
模式识别(心理学)
语音识别
算法
地质学
化学
语言学
大地测量学
哲学
有机化学
作者
Zheng Lian,Licai Sun,Haiyang Sun,Licai Sun,Zhihua Wen,Hong Gu,Bin Liu,Jianhua Tao
标识
DOI:10.1016/j.inffus.2024.102367
摘要
Recently, GPT-4 with Vision (GPT-4V) has demonstrated remarkable visual capabilities across various tasks, but its performance in emotion recognition has not been fully evaluated. To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition. This paper collectively refers to these tasks as "Generalized Emotion Recognition (GER)". Through experimental analysis, we observe that GPT-4V exhibits strong visual understanding capabilities in GER tasks. Meanwhile, GPT-4V shows the ability to integrate multimodal clues and exploit temporal information, which is also critical for emotion recognition. However, it's worth noting that GPT-4V is primarily designed for general domains and cannot recognize micro-expressions that require specialized knowledge. To the best of our knowledge, this paper provides the first quantitative assessment of GPT-4V for GER tasks. We have open-sourced the code and encourage subsequent researchers to broaden the evaluation scope by including more tasks and datasets. Our code and evaluation results are available at: https://github.com/zeroQiaoba/gpt4v-emotion.
科研通智能强力驱动
Strongly Powered by AbleSci AI