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GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition

Core Concepts
GPT-4V demonstrates strong visual understanding capabilities in Generalized Emotion Recognition tasks, but struggles with specialized knowledge like micro-expressions.
This article evaluates GPT-4V's performance in emotion recognition tasks across various datasets. It discusses the model's ability to integrate multimodal clues and exploit temporal information. The study highlights the limitations of GPT-4V in recognizing micro-expressions and provides insights into potential future research directions. Structure: Introduction: Discusses the importance of emotion recognition and introduces Generalized Emotion Recognition (GER) tasks. Related Works: Explores different tasks within GER and their distinctions. Task Description: Details each task and dataset used for evaluation. GPT-4V Calling Strategy: Describes the strategy designed for handling requests in GER tasks. Results and Discussion: Presents main results, including comparisons with baselines and supervised systems. Temporal Modeling Ability: Evaluates GPT-4V's performance based on sampling frames in dynamic facial emotion recognition. Multimodal Fusion Ability: Examines GPT-4V's ability to integrate multimodal information in emotion recognition tasks. System Stability: Analyzes the stability of GPT-4V predictions through multiple runs. Class-wise Performance Analysis: Visualizes confusion matrices to analyze class-wise prediction consistency. Robustness to Template Change: Explores how changes in prompt templates affect GPT-4V's performance. Robustness to Color Space: Evaluates GPT-4V's robustness to color space changes using grayscale images. Security Check: Discusses instances where security checks impact model predictions. Case Study: Provides examples of incorrect predictions made by GPT-4V in different tasks.
"Through experimental analysis, we observe that GPT-4V exhibits strong visual understanding capabilities in GER tasks." "GPT-4V is primarily designed for general domains and cannot recognize micro-expressions that require specialized knowledge."

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by Zheng Lian,L... at 03-19-2024
GPT-4V with Emotion

Deeper Inquiries






色空間変更時でも同等程度良好パフォーマンス展開した点から見ても色空間変更後でも安定した予測能力持つこと確認可能です。 しかし,特定カラー・ラベル(恐怖や嫌悪)等他カラースペース下不正確予測多く発生. その理由:不正確予測多く発生原因主要部分,低成果率カテゴリー内ランダム性高い. 今後:混乱行列及び実際評価値比較解析進行推奨,模型全体パフォーマンス把握重要.