Aug 21, 2024 · We then propose a novel MLLM, named EMO-LLaMA, which incorporates facial priors from a pretrained facial analysis network to enhance human facial information. Specifically, we design a Face Info Mining module to extract both global and local facial information.

By aligning features into a shared space and employing a modified LLaMA model with instruction tuning, Emotion-LLaMA significantly enhances both emotional recognition and reasoning capabilities.

Official Implementation for EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning Resources

Jun 17, 2024 · By aligning features into a shared space and employing a modified LLaMA model with instruction tuning, Emotion-LLaMA significantly enhances both emotional recognition and reasoning capabilities.

Official Implementation for EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning - xxtars/EMO-LLaMA

Aug 21, 2024 · We then propose a novel MLLM, named EMO-LLaMA, which incorporates facial priors from a pretrained facial analysis network to enhance human facial information. Specifically, we design a Face Info Mining module to extract both global and local facial information.

Emollama-7b is part of the EmoLLMs project, the first open-source large language model (LLM) series for comprehensive affective analysis with instruction-following capability. This model is finetuned based on the Meta LLaMA2-7B foundation model and the …

本文针对面部表情识别（fer）在泛化能力和语义信息对齐方面的不足，提出了一种新颖的多模态大型语言模型emo-llama。 通过使用预训练的面部分析网络和设计面部信息挖掘模块，实验表明EMO-LLaMA在静态和动态FER数据集上均达到与现有最先进方法相媲美的性能。

Aug 21, 2024 · We then propose a novel MLLM, named EMO-LLaMA, which incorporates facial priors from a pretrained facial analysis network to enhance human facial information. Specifically, we design a Face Info Mining module to extract both global and local facial information.

Facial expression recognition (FER) is an important research topic in emotional artificial intelligence. In recent decades, researchers have made remarkable progress. However, current FER paradigms face challenges in generalization, lack semantic information aligned with natural language, and struggle to process both images and videos within a unified framework, making their application in ...