Columbia U’s ViperGPT Solves Complex Visual Queries via Python Execution

Computer vision models have made remarkable progress in recent years on fundamental tasks like object recognition and depth estimation — but still struggle with visual queries that require both visual processing and reasoning. While end-to-end models remain the typical approach in this research area, their limited interpretability and generalization abilities leave them underequipped for improving performance on complex visual queries.

In the new paper ViperGPT: Visual Inference via Python Execution for Reasoning, a Columbia University research team presents ViperGPT, a framework for solving complex visual queries by integrating code-generation models into vision via a Python interpreter. The proposed approach requires no additional training and achieves state-of-the-art results.