ViperGPT: Visual Inference via Python Execution for Reasoning

Dídac Surís, Sachit Menon, Carl Vondrick

Abstract: Answering visual queries is a complex task that requires both visual processing and reasoning. End-to-end models, the dominant approach for this task, do not explicitly differentiate between the two, limiting interpretability and generalization. Learning modular programs presents a promising alternative, but has proven challenging due to the difficulty of learning both the programs and modules simultaneously. We introduce ${\color{green}{\text{ViperGPT}}}$, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query. ${\color{green}{\text{ViperGPT}}}$ utilizes a provided API to access the available modules, and composes them by generating Python code that is later executed. This simple approach requires no further training, and achieves state-of-the-art results across various complex visual tasks.