Zhifeng Wang
2024
Visual Prompting in LLMs for Enhancing Emotion Recognition
Qixuan Zhang
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Zhifeng Wang
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Dylan Zhang
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Wenjia Niu
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Sabrina Caldwell
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Tom Gedeon
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Yang Liu
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Zhenyue Qin
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Vision Large Language Models (VLLMs) are transforming the intersection of computer vision and natural language processing; however, the potential of using visual prompts for emotion recognition in these models remains largely unexplored and untapped. Traditional methods in VLLMs struggle with spatial localization and often discard valuable global context. We propose a novel Set-of-Vision prompting (SoV) approach that enhances zero-shot emotion recognition by using spatial information, such as bounding boxes and facial landmarks, to mark targets precisely. SoV improves accuracy in face count and emotion categorization while preserving the enriched image context. Through comprehensive experimentation and analysis of recent commercial or open-source VLLMs, we evaluate the SoV model’s ability to comprehend facial expressions in natural environments. Our findings demonstrate the effectiveness of integrating spatial visual prompts into VLLMs for improving emotion recognition performance.
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Co-authors
- Qixuan Zhang 1
- Dylan Zhang 1
- Wenjia Niu 1
- Sabrina Caldwell 1
- Tom Gedeon 1
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