EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning Visual Instruction Tuning (VIT) is a novel learning paradigm that fine-tunes pre-trained language models using task-specific instructions. This approach has shown promising zero-shot results in various natural language processing tasks but has not been explored in vision emotion understanding. This paper focuses on enhancing the model's proficiency in understanding and adhering to instructions related to emotional contexts. The authors identify key visual clues critical to visual emotion recognition and introduce a GPT-assisted pipeline for generating emotion visual instruction data, addressing the scarcity of annotated instruction data in this domain. Building on the InstrucBLIP framework, the proposed EmoVIT architecture incorporates emotion-specific instruction data, leveraging the capabilities of Large Language Models (LLMs) to improve performance. Extensive experiments demonstrate the model's proficiency in emotion classification, affective reasoning, and humor comprehension. The comparative analysis provides a robust benchmark for Emotion Visual Instruction Tuning, offering valuable insights and directions for future research. The code for EmoVIT is available at <https://github.com/aimmemotion/EmoVIT>.EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning Visual Instruction Tuning (VIT) is a novel learning paradigm that fine-tunes pre-trained language models using task-specific instructions. This approach has shown promising zero-shot results in various natural language processing tasks but has not been explored in vision emotion understanding. This paper focuses on enhancing the model's proficiency in understanding and adhering to instructions related to emotional contexts. The authors identify key visual clues critical to visual emotion recognition and introduce a GPT-assisted pipeline for generating emotion visual instruction data, addressing the scarcity of annotated instruction data in this domain. Building on the InstrucBLIP framework, the proposed EmoVIT architecture incorporates emotion-specific instruction data, leveraging the capabilities of Large Language Models (LLMs) to improve performance. Extensive experiments demonstrate the model's proficiency in emotion classification, affective reasoning, and humor comprehension. The comparative analysis provides a robust benchmark for Emotion Visual Instruction Tuning, offering valuable insights and directions for future research. The code for EmoVIT is available at <https://github.com/aimmemotion/EmoVIT>.