Multi-modal large language models (MLLMs) have achieved remarkable
performance on objective multimodal perception tasks, but their ability to
interpret subjective, emotionally nuanced multimodal content remains largely
unexplored. Thus, it impedes their ability to effectively understand and react
to the intricate emotions expressed by humans through multimodal media. To
bridge this gap, we introduce EmoBench, the first comprehensive benchmark
designed specifically to evaluate the emotional capabilities of MLLMs across
five popular emotional tasks, using a diverse dataset of 287k images and videos
paired with corresponding textual instructions. Meanwhile, we propose EmoLLM, a
novel model for multimodal emotional understanding, incorporating with two core
techniques. 1) Multi-perspective Visual Projection, it captures diverse
emotional cues from visual data from multiple perspectives. 2) EmoPrompt, it
guides MLLMs to reason about emotions in the correct direction. Experimental
results demonstrate that EmoLLM significantly elevates multimodal emotional
understanding performance, with an average improvement of 12.1% across multiple
foundation models on EmoBench. Our work contributes to the advancement of MLLMs
by facilitating a deeper and more nuanced comprehension of intricate human
emotions, paving the way for the development of artificial emotional
intelligence capabilities with wide-ranging applications in areas such as
human-computer interaction, mental health support, and empathetic AI systems.
Code, data, and model will be released.

通过引入 EmoBench 和 EmoLLM，基于多模态大型语言模型的多模态情感理解性能得到显著提升，提供了更深入、更细致的人类情感理解，为人机交互、心理健康支持和共情人工智能系统等领域的应用开发提供了可能。

EmoLLM：多模态情感理解与大型语言模型

EmoLLM: Multimodal Emotional Understanding Meets Large Language Models

As social robots and other intelligent machines enter the home, artificial
emotional intelligence (AEI) is taking center stage to address users' desire
for deeper, more meaningful human-machine interaction. To accomplish such
efficacious interaction, the next-generation AEI need comprehensive human
emotion models for training. Unlike theory of emotion, which has been the
historical focus in psychology, emotion models are a descriptive tools. In
practice, the strongest models need robust coverage, which means defining the
smallest core set of emotions from which all others can be derived. To achieve
the desired coverage, we turn to word embeddings from natural language
processing. Using unsupervised clustering techniques, our experiments show that
with as few as 15 discrete emotion categories, we can provide maximum coverage
across six major languages--Arabic, Chinese, English, French, Spanish, and
Russian. In support of our findings, we also examine annotations from two
large-scale emotion recognition datasets to assess the validity of existing
emotion models compared to human perception at scale. Because robust,
comprehensive emotion models are foundational for developing real-world
affective computing applications, this work has broad implications in social
robotics, human-machine interaction, mental healthcare, and computational
psychology.

研究自然语言处理中的词向量技术如何应用于人类情感模型，以在全球六种主要语言中提供最大覆盖范围，并评估现有情感模型与人类感知的有效性。