# ViperGPT: Visual Inference via Python Execution for Reasoning

**Authors:** Dídac Surís, Sachit Menon, Carl Vondrick  
**arXiv:** [2303.08128](https://arxiv.org/abs/2303.08128)  
**Date:** 2023-03-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.08128v1/

## Key points

- Authors introduce ViperGPT framework for visual inference via Python execution
- End-to-end models lack interpretability and generalization due to not differentiating between visual processing and reasoning
- ViperGPT uses code-generation models to compose vision-and-language models into subroutines for query results
- Framework breaks learning process into manageable steps, simplifying learning of programs and modules simultaneously
- Achieves state-of-the-art results in complex visual tasks without additional training
- Combines visual processing with reasoning through code generation for enhanced interpretability and generalization

## Summary

In their paper titled "ViperGPT: Visual Inference via Python Execution for Reasoning," authors Dídac Surís, Sachit Menon, and Carl Vondrick introduce the novel framework of ViperGPT to address the challenging task of answering visual queries that require both visual processing and reasoning. The limitations of end-to-end models are highlighted as they do not explicitly differentiate between these two components, resulting in reduced interpretability and generalization. To overcome this issue, ViperGPT utilizes a unique approach by leveraging code-generation models to compose vision-and-language models into subroutines for generating results for any given query. This framework utilizes a provided API to access available modules and dynamically composes them by generating Python code that is subsequently executed. By breaking down the learning process into manageable steps, ViperGPT simplifies the task of learning both programs and modules simultaneously. As a result, it achieves state-of-the-art results across various complex visual tasks without requiring additional training. The ability of ViperGPT to combine visual processing with reasoning through code generation demonstrates its effectiveness in enhancing interpretability and generalization in addressing visual queries. This innovative strategy opens up new possibilities for advancing research in the field of vision-and-language modeling by introducing a more structured and efficient approach to tackling complex visual tasks.