VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images

TL;DR

VisionFoundry introduces a synthetic data pipeline that enhances vision-language models' visual perception by generating targeted training data from task keywords, improving performance on perception benchmarks.

Contribution

The paper presents a novel synthetic data generation method using large language models and text-to-image synthesis, creating a new dataset that improves VLMs' visual perception capabilities.

Findings

Models trained on VisionFoundry-10K improve perception benchmarks by up to 10%.

Synthetic supervision addresses low-level visual skill limitations in VLMs.

The approach scales favorably with increased data size.

Abstract

Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contributing factor is that natural image datasets provide limited supervision for low-level visual skills. This motivates a practical question: can targeted synthetic supervision, generated from only a task keyword such as Depth Order, address these weaknesses? To investigate this question, we introduce VisionFoundry, a task-aware synthetic data generation pipeline that takes only the task name as input and uses large language models (LLMs) to generate questions, answers, and text-to-image (T2I) prompts, then synthesizes images with T2I models and verifies consistency with a proprietary VLM, requiring no reference images or human annotation. Using VisionFoundry, we construct VisionFoundry-10K, a synthetic visual question answering (VQA) dataset containing 10k image-question-answer triples spanning 10 tasks. Models trained on VisionFoundry-10K achieve substantial improvements on visual perception benchmarks: +7% on MMVP and +10% on CV-Bench-3D, while preserving broader capabilities and showing favorable scaling behavior as data size increases. Our results suggest that limited task-targeted supervision is an important contributor to this bottleneck and that synthetic supervision is a promising path toward more systematic training for VLMs.