Visual Prompt Engineering Boosts Video Model Performance
A new paper on arXiv (2607.25537) introduces Visual Prompt Engineering (VIPE), a technique that automatically modifies task images to improve video model performance. The authors argue that as video models become foundation models for visual tasks like physics reasoning, they benefit from visual prompt engineering similarly to how language models benefit from text prompts. For instance, an abstract sketch of a ball passing obstacles can be transformed into a photorealistic image using an image editing model, enhancing the video model's reasoning accuracy. Experiments show VIPE outperforms classic text-based prompt engineering and test-time scaling across various tasks. The paper suggests that visual prompt engineering is a critical method for optimizing video foundation models.
Key facts
- arXiv paper 2607.25537 introduces Visual Prompt Engineering (VIPE).
- VIPE automatically modifies task images to improve video model performance.
- Video models are becoming foundation models for visual tasks like physics reasoning.
- An abstract sketch can be turned into a photorealistic version using an image editing model.
- VIPE improves video reasoning performance across tasks.
- VIPE can be more effective than text-based prompt engineering or test-time scaling.
- The paper is from arXiv, a preprint repository.
- The technique is analogous to prompt engineering for language models.
Entities
Institutions
- arXiv