Papers › ViperGPT: Visual Inference via Python Execution for Reasoning
ViperGPT: Visual Inference via Python Execution for Reasoning
Dídac Surís, Sachit Menon, Carl Vondrick
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 ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query. 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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Question Answering | NExT-QA | ViperGPT(0-shot) | Accuracy | 60.0 | #36 of 47 | Archive leaderboard | report |
| Zero-Shot Video Question Answer | NExT-QA | ViperGPT (GPT-3.5) | Accuracy | 60.0 | #23 of 27 | Archive leaderboard | report |
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